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Journal articles on the topic 'Collision Optimizer'
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Anshu Vashisth. "Energy-Efficient Hybrid Bio-Inspired Approach for Low-Latency Collision-Aware UAV Networks." Journal of Information Systems Engineering and Management 10, no. 39s (2025): 128–44. https://doi.org/10.52783/jisem.v10i39s.7079.
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The pervasive integration of Unmanned Aerial Vehicle (UAV) networks across various applications underscores the imperative for sophisticated communication and collision avoidance strategies to optimize their operational prowess. Traditional UAV network optimization methodologies grapple with inherent challenges related to collision minimization and channel utilization, resulting in detrimental outcomes such as elevated communication delays, increased energy consumption, and compromised throughput alongside diminished packet delivery ratios. This study addresses these shortcomings through the introduction of an innovative optimization model that synergizes the robust characteristics of the Teacher Learner-based Grey Wolf Optimizer (TLGWO) and the Bat Firefly Optimizer (BFFO), thereby significantly elevating the overall performance of UAV networks. The TLGWO component of the pro-posed model is intricately designed to minimize collisions among UAV nodes by analytically assessing temporal and spatial performance metrics. This includes a nuanced examination of communication delay dynamics and the historical context of avoided collisions. Simultaneously, the BFFO module is engineered to maximize channel utilization, leveraging the same performance metrics for a holistic optimization approach. The dual application of TLGWO and BFFO ensures a comprehensive enhancement of UAV network efficiency. Empirical validation demonstrates the superiority of the proposed model over existing methods, showcasing a remarkable 10.4% reduction in communication delay, an 8.5% improvement in energy efficiency, a 3.5% increase in packet delivery ratio, a 9.5% enhancement in throughput, and a 4.9% reduction in collision occurrences. The significant impact of this research is far-reaching, providing a robust and versatile framework for fortifying UAV network efficiency across diverse applications, thereby propelling the field towards more dependable and efficient UAV deployments in critical sectors.
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Chen, Yidong, Jinghua Li, Lei Zhou, Dening Song, and Boxin Yang. "An Improved Dung Beetle Optimizer for the Twin Stacker Cranes’ Scheduling Problem." Biomimetics 9, no. 11 (2024): 683. http://dx.doi.org/10.3390/biomimetics9110683.
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In recent years, twin stacker crane units have been increasingly integrated into large automated storage and retrieval systems (AS/RSs) in shipyards to enhance operational efficiency. These common rail units often encounter conflicts, and the additional time costs incurred during collision avoidance significantly diminish AS/RS efficiency. Therefore, addressing the twin stacker cranes’ scheduling problem (TSSP) with a collision-free constraint is essential. This paper presents a novel approach to identifying and avoiding collisions by approximating the stacker crane’s trip trajectory as a triangular envelope. Utilizing the collision identification equation derived from this method, we express the collision-free constraint within the TSSP and formulate a mixed-integer programming model. Recognizing the multimodal characteristics of the TSSP objective function, we introduce the dung beetle optimizer (DBO), which excels in multimodal test functions, as the foundational framework for a heuristic optimizer aimed at large-scale TSSPs that are challenging for exact algorithms. To adapt the optimizer for bi-level programming problems like TSSPs, we propose a double-layer code mechanism and innovatively design a binary DBO for the binary layer. Additionally, we incorporate several components, including a hybrid initialization strategy, a Cauchy–Gaussian mixture distribution neighborhood search strategy, and a velocity revision strategy based on continuous space discretization, into the improved dung beetle optimizer (IDBO) to further enhance its performance. To validate the efficacy of the IDBO, we established a numerical experimental environment and generated a series of instances based on actual environmental parameters and operational conditions from an advanced AS/RS in southeastern China. Extensive comparative experiments on various scales and distributions demonstrate that the components of the IDBO significantly improve algorithm performance, yielding stable advantages over classical algorithms in solving TSSPs, with improvements exceeding 10%.
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Lim, Sanghyeon, Wontaek Oh, Jihun Choi, and Jongchan Park. "Estimation Collision Speed of Vehicle by Using PC-CRASH Collision Optimizer." Transaction of the Korean Society of Automotive Engineers 27, no. 12 (2019): 911–17. http://dx.doi.org/10.7467/ksae.2019.27.12.911.
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Raman Dugyala, Akhil Khare, K. Selvakumar,. "Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 2431–44. http://dx.doi.org/10.52783/tjjpt.v44.i3.722.
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A wireless sensor network (WSN) consists of distributed autonomous sensors deployed over a large geographical area. Wireless Sensor Networks (WSNs) are composed of large-scale sensors that are specifically assigned to do particular tasks, with a significant portion of these tasks involving reporting and monitoring activities. Nevertheless, because to the potential expansion of the network to several sensor nodes, the likelihood of collision is significantly increased. This research presents an innovative approach for conducting collision detection and mitigation in wireless sensor networks (WSN). The first step involves conducting a simulation of Wireless Sensor Networks (WSN), followed by the utilization of the Fractional Artificial Bee Colony (FABC) algorithm for the selection of cluster heads. In this context, the network-based parameter is derived by considering factors such as the Received Signal Strength Index (RSSI), priority level, delivery rate, and energy consumption. The Deep Recurrent Neural Network (DRNN) has been modified to suit the task of collision detection. The training of the deep recurrent neural network (DRNN) is conducted via the Lion Crow Search optimizer (LCSO). Following the completion of collision detection, the subsequent step involves the implementation of a collision mitigation process utilizing a pre-scheduling technique known as Dolphin Ant Lion Optimizer (Dolphin ALO). In this context, the evaluation of fitness encompasses many factors related to collision mitigation, including energy consumption, Sleep Index (SI), delivery rate, priority level, E-waste management, and E-save measures. The approach presented in this study demonstrated superior performance in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and collision detection rate. Specifically, it achieved the least energy consumption of 0.185, the greatest throughput of 0.815, the highest PDR of 0.815, and the highest collision detection rate of 0.930.
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Hawary, A. F., and N. A. Razak. "Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs." IOP Conference Series: Materials Science and Engineering 370 (May 2018): 012043. http://dx.doi.org/10.1088/1757-899x/370/1/012043.
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Duan, Shaomi, Huilong Luo, and Haipeng Liu. "An Elastic Collision Seeker Optimization Algorithm for Optimization Constrained Engineering Problems." Mathematical Problems in Engineering 2022 (January 10, 2022): 1–28. http://dx.doi.org/10.1155/2022/1344667.
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To improve the seeker optimization algorithm (SOA), an elastic collision seeker optimization algorithm (ECSOA) was proposed. The ECSOA evolves some individuals in three situations: completely elastic collision, completely inelastic collision, and non-completely elastic collision. These strategies enhance the individuals’ diversity and avert falling into the local optimum. The ECSOA is compared with the particle swarm optimization (PSO), the simulated annealing and genetic algorithm (SA_GA), the gravitational search algorithm (GSA), the sine cosine algorithm (SCA), the multiverse optimizer (MVO), and the seeker optimization algorithm (SOA); then, fifteen benchmark functions, four PID control parameter models, and six constrained engineering optimization problems were selected for the experiment. According to the experimental results, the ECSOA can be used in the benchmark functions, the PID control parameter optimization, and the optimization constrained engineering problems. The optimization ability and robustness of ECSOA are better.
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Chen, Zhongcheng, Chuang Li, Meiling Qin, and Yong Wang. "DBO-Based Mobile Agent Crowd Sensing for Pollution Source Localization." Journal of Physics: Conference Series 3004, no. 1 (2025): 012093. https://doi.org/10.1088/1742-6596/3004/1/012093.
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Abstract Pollution source localization is a critical concern in various monitoring applications. In this paper, we propose a multi-agent crowd sensing method for pollution source localization using simplified dung beetle optimizer (DBO). The DBO controller guides the agent swarm to move towards the pollution source in the manner of aggregation, meanwhile, prevents the occurrence of collisions between agents or between agents and obstacles during movement. Besides, the controller incorporates the remaining to ensure balanced energy cost among mobile agents. Furthermore, a collaborative sensing model based on link quality is specially designed to maintain reliable connectivity between mobile agents during pollution source localization. Experiment results demonstrate our method enables precise localization of the pollution source, while having superiority in energy efficiency, collision avoidance and multi-agent connectivity.
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Zhong, Weifan, and Lijing Du. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads." Sustainability 15, no. 4 (2023): 2944. http://dx.doi.org/10.3390/su15042944.
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Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.
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Jarray, Raja, Soufiene Bouallègue, Hegazy Rezk, and Mujahed Al-Dhaifallah. "Parallel Multiobjective Multiverse Optimizer for Path Planning of Unmanned Aerial Vehicles in a Dynamic Environment with Moving Obstacles." Drones 6, no. 12 (2022): 385. http://dx.doi.org/10.3390/drones6120385.
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Path planning with collision avoidance for unmanned aerial vehicles (UAVs) in environments with moving obstacles is a complex process of navigation, often considered a hard optimization problem. Ordinary resolution algorithms may fail to provide flyable and collision-free paths under the time-consumption constraints required by the dynamic 3D environment. In this paper, a new parallel multiobjective multiverse optimizer (PMOMVO) is proposed and successfully applied to deal with the increased computation time of the UAV path planning problem in dynamic 3D environments. Collision constraints with moving obstacles and narrow pass zones were established based on a mathematical characterization of any intersection with lines connecting two consecutive drones’ positions. For the implementation, a multicore central processing unit (CPU) architecture was proposed according to the concept of master–slave processing parallelization. Each subswarm of the entire PMOMVO population was granted to a corresponding slave, and representative solutions were selected and shared with the master core. Slaves sent their local Pareto fronts to the CPU core representing the master that merged the received set of nondominated solutions and built a global Pareto front. Demonstrative results and nonparametric ANOVA statistical analyses were carried out to show the effectiveness and superiority of the proposed PMOMVO algorithm compared to other homologous, multiobjective metaheuristics.
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Zhang, Tian, and Xiangyin Zhang. "Distributed Model Predictive Control with Particle Swarm Optimizer for Collision-Free Trajectory Tracking of MWMR Formation." Actuators 12, no. 3 (2023): 127. http://dx.doi.org/10.3390/act12030127.
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The distributed model predictive control (DMPC) strategy with particle swarm optimization (PSO) is applied to solve the collision-free trajectory tracking problem for the mecanum-wheeled mobile robot (MWMR) formation. Under the leader–follower framework, the predictive model is established considering the kinematics and dynamics of the MWMR with the uncertainties and external disturbances. Based on the information from itself and its neighbors, each MWMR is assigned its own finite-horizon optimal control problem, of which the objective/cost function consists of formation maintenance, trajectory tracking, and collision avoidance terms, and the control inputs of each MWMR are computed synchronously in a distributed manner. PSO serves as the fast and effective optimizer to find feasible solutions to these finite-horizon optimal control problems. Further, the feedback emendation is implemented using a double closed-loop compensator to efficiently inhibit the influence of unknown dynamics in real time. The stability of the proposed distributed formation control approach is strictly analyzed. Numerical simulations confirmed the robustness and effectiveness of the control approach in obstacle environments.
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Sarah, Saadoon Jasim, and Karim Abdul Hassan Alia. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6034–44. https://doi.org/10.11591/ijece.v12i6.pp6034-6044.
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In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.
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Keanly, Oran, and Jacobus Adriaan Albertus Engelbrecht. "Optimization-based path planning and collision avoidance for autonomous racing." MATEC Web of Conferences 388 (2023): 04018. http://dx.doi.org/10.1051/matecconf/202338804018.
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This paper presents a hierarchical motion planner for autonomous racing. The long-term motion planner functions offline and formulates the optimal motion plan for the entire race track. The short-term collision avoidance planner functions online and formulates a motion plan for a limited horizon ahead of the autonomous car when an obstacle is detected in the path of the vehicle. The motion planners formulate the planning problems as optimal control problems and solve the resulting optimizations using an interior point optimizer (IPOPT). Simulation experiments show that an autonomous vehicle using the motion planner is able to race around the track with minimum lap time while avoiding unexpected obstacles.
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Liang, J. J., H. Song, B. Y. Qu, and Z. F. Liu. "Comparison of Three Different Curves Used in Path Planning Problems Based on Particle Swarm Optimizer." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/623156.
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In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria (the shortest path length, security, feasibility, smoothness, and so on), so defining a suitable curve to describe path is essential. Three different commonly used curves are compared and discussed based on their performance on solving a set of path planning problems. Dynamic multiswarm particle swarm optimizer is employed to optimize the necessary parameters for these curves. The results show that Bezier curve is the most suitable curve for producing path for the certain path planning problems discussed in this paper. Safety criterion is considered as a constrained condition. A new constraint handling method is proposed and compared with other two constraint handling methods. The results show that the new method has a better characteristic to improve the performance of algorithm.
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Jasim, Sarah Saadoon, and Alia Karim Abdul Hassan. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6034. http://dx.doi.org/10.11591/ijece.v12i6.pp6034-6044.
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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>
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Wang, Zongshan, and Hongwei Ding. "Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning." International Journal of Robotics and Automation Technology 10 (September 22, 2023): 64–74. http://dx.doi.org/10.31875/2409-9694.2023.10.06.
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Abstract: The objective of mobile robot path planning (MRPP) is to devise the shortest obstacle-free path for autonomous mobile robots based on a given terrain. Numerous MRPP methods have been extensively researched. This paper presents a novel approach called Opposition-based Learning Equilibrium Optimizer (OEO) for generating smooth paths for mobile robots. The fundamental idea behind OEO is to introduce an opposition-based learning mechanism while maintaining the overall framework of the basic EO algorithm. This modification alleviates the susceptibility of the basic EO algorithm to local optima. The OEO algorithm is employed to provide smooth paths for autonomous mobile robots, and the results are compared with several classical metaheuristic algorithms. Comparative analysis across different environments demonstrates that the proposed OEO-based path planning method consistently yields the shortest and most collision-free paths with superior stability.
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Machmudah, Affiani, and Setyamartana Parman. "Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization." International Journal of Artificial Intelligence & Robotics (IJAIR) 3, no. 1 (2021): 1–14. http://dx.doi.org/10.25139/ijair.v3i1.3821.
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 A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.
 
 
 
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Jasim, Sarah S., Alia K. Abdul Hassan, and Scott Turner. "Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (2022): 49–56. http://dx.doi.org/10.14500/aro.10928.
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It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.
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Zhang, Yong Gang, Jian Min Xu, Tie Fang Zou, and Yu Liu. "A Method for Reconstructing Vehicle - Vehicle Impact Accidents Based on Pc-Crash." Applied Mechanics and Materials 641-642 (September 2014): 799–804. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.799.
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To reconstruct accident succinctly and scientifically, four steps of vehicle-vehicle impact accident reconstruction based on Pc-Crash were discussed, including accident scene reconstruction, vehicle modeling, accident reconstruction, result analysis and verification. Three accident scene reconstruction methods were proposed. Key attention was paid to methods which can reduce difficulties of accident reconstruction, such as drive model, equivalent energy speed (EES) database and collision optimizer. Finally, a case study was conducted, which confirmed that the proposed four steps can simplify vehicle-vehicle accident reconstruction while maintaining satisfying objectivity and reliability. This paper can contribute to the better application of Pc-Crash to traffic accident reconstruction.
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Jiang, Wei, Yongxi Lyu, Yongfeng Li, Yicong Guo, and Weiguo Zhang. "UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer." Aerospace Science and Technology 121 (February 2022): 107314. http://dx.doi.org/10.1016/j.ast.2021.107314.
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Gao, Yujie, Zhichun Li, Haorui Wang, et al. "An Improved Spider-Wasp Optimizer for Obstacle Avoidance Path Planning in Mobile Robots." Mathematics 12, no. 17 (2024): 2604. http://dx.doi.org/10.3390/math12172604.
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The widespread application of mobile robots holds significant importance for advancing social intelligence. However, as the complexity of the environment increases, existing Obstacle Avoidance Path Planning (OAPP) methods tend to fall into local optimal paths, compromising reliability and practicality. Therefore, based on the Spider-Wasp Optimizer (SWO), this paper proposes an improved OAPP method called the LMBSWO to address these challenges. Firstly, the learning strategy is introduced to enhance the diversity of the algorithm population, thereby improving its global optimization performance. Secondly, the dual-median-point guidance strategy is incorporated to enhance the algorithm’s exploitation capability and increase its path searchability. Lastly, a better guidance strategy is introduced to enhance the algorithm’s ability to escape local optimal paths. Subsequently, the LMBSWO is employed for OAPP in five different map environments. The experimental results show that the LMBSWO achieves an advantage in collision-free path length, with 100% probability, across five maps of different complexity, while obtaining 80% fault tolerance across different maps, compared to nine existing novel OAPP methods with efficient performance. The LMBSWO ranks first in the trade-off between planning time and path length. With these results, the LMBSWO can be considered as a robust OAPP method with efficient solving performance, along with high robustness.
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Zbiss, Khalil, Amal Kacem, Mario Santillo, and Alireza Mohammadi. "Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting." Applied Sciences 14, no. 19 (2024): 8614. http://dx.doi.org/10.3390/app14198614.
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This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.
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Wang, Fang, Liang Zhao, and Yong Bai. "Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer." Mathematical Problems in Engineering 2022 (November 18, 2022): 1–15. http://dx.doi.org/10.1155/2022/1283374.
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The appeal for safe navigation of autonomous surface vessels (ASVs) has deemed the path planning problem as an attractive research interest. However, most of the previous works to solve the path planning problem focus on finding the shortest and collision-free path, but the solutions are scarcely satisfied by the safety requirements and constraints related to the USVs’ mechanical systems. To address this challenge, we present a novel path planning method based on a modified artificial fish swarm algorithm in combination with a path optimizer. The modifications are made from two perspectives: (1) Four customized operators and an adaptive factor are applied to improve the convergence performance of the algorithm. (2) A local path optimizer is proposed to enhance its feasibility of cooperating with the USV control system. Path safety, path smoothness, and nonholonomic constraints are considered. The path planning benchmark experiments have demonstrated its superior performance in terms of efficiency and path quality compared to other state-of-the-art algorithms. Moreover, the proposed method is also integrated into the USV’s control system in a practical environment with satisfactory feasibility. The simulation results provide strong evidence that the proposed method can be regarded as a practical approach for USV path planning problems.
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Sun, Qingbin, Xitai Na, Zhihui Feng, Shiji Hai, and Jinshuo Shi. "Three-Dimensional UAV Path Planning Based on Multi-Strategy Integrated Artificial Protozoa Optimizer." Biomimetics 10, no. 4 (2025): 201. https://doi.org/10.3390/biomimetics10040201.
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Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper proposes a multi-strategy integrated artificial protozoa optimization (IAPO) algorithm for UAV 3D path planning. First, the tent map and refractive opposition-based learning (ROBL) are employed to enhance the diversity and quality of the initial population. Second, in the algorithm’s autotrophic foraging stage, we design a dynamic optimal leadership mechanism, which accelerates the convergence speed while ensuring robust exploration capability. Additionally, during the reproduction phase of the algorithm, we update positions using a Cauchy mutation strategy. Thanks to the heavy-tailed nature of the Cauchy distribution, the algorithm is less likely to become trapped in local optima during exploration, thereby increasing the probability of finding the global optimum. Finally, we incorporate the simulated annealing algorithm into the heterotrophic foraging and reproduction stages, effectively preventing the algorithm from getting trapped in local optima and reducing the impact of inferior solutions on the convergence efficiency. The proposed algorithm is validated through comparative experiments using 12 benchmark functions from the 2022 IEEE Congress on Evolutionary Computation (CEC), outperforming nine common algorithms in terms of convergence speed and optimization accuracy. The experimental results also demonstrate IAPO’s superior performance in generating collision-free and energy-efficient UAV paths across diverse 3D environments.
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Chen, Ruinan, Jie Hu, and Wencai Xu. "An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles." Applied Sciences 12, no. 23 (2022): 11982. http://dx.doi.org/10.3390/app122311982.
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It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.
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Wadood, Abdul, Al-Fahad Yousaf, and Aadel Mohammed Alatwi. "An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach." Fractal and Fractional 8, no. 6 (2024): 334. http://dx.doi.org/10.3390/fractalfract8060334.
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This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.
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Cristian-Dumitru, Avatavului, Ifrim Rareş-Cristian, and Voncilă Mihai. "Can Neural Networks Enhance Physics Simulations?" BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 2 (2024): 76–92. https://doi.org/10.18662/brain/14.2/445.
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<em>The primary objective of this research manuscript is to design, develop, and evaluate an artificial neural network architecture that is capable of emulating and predicting the dynamic interaction patterns manifested during the encounter between two distinct entities. This endeavor is primarily centered around computational learning and understanding of the associated physical impulses that emerge when these objects engage in contact, elucidating the complex physical interplays therein. This process incorporates the strategic use of an extant physics engine to generate the requisite training datasets, thereby providing a robust and comprehensive foundation for neural network training and subsequent performance evaluation. In order to scrutinize and substantiate the effectiveness of the proposed artificial neural network model, this investigation also embarks on a rigorous comparative analysis. The principal focus of this comparison is to juxtapose the results rendered by the trained neural network vis-a-vis those produced by the original physics engine. The goal here is to gauge the precision, reliability, and practicality of the trained model in accurately predicting the physical impulses, thereby demonstrating its potential to stand as a feasible alternative to the traditional physics engine. Despite the initial success of this endeavor, it is worth noting that the proposed neural network system managed to achieve a range of prediction rates, oscillating between 60% and 91%, contingent upon the specific test scenario. While these preliminary results are promising, they elucidate the necessity for further optimization and refinement to bolster the model's performance and prediction accuracy.</em>
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Mahajan, V. "ResQAlert: Proficient Crash Detection and Alert System." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1378–85. http://dx.doi.org/10.22214/ijraset.2023.56688.
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Abstract: In response to the urgent need for automated vehicle crash detection and its potential to increase road safety while reducing crash severity, this study introduces an innovative and novel approach. It focuses on using deep learning techniques for image recognition in videos to predict the likelihood of an accident. Specifically, a convolutional neural network (CNN) model was carefully constructed using TensorFlow and Keras and trained on a diverse and carefully annotated dataset covering a wide range of road scenarios. These scenarios include different conditions such as the presence of vehicles, pedestrians and different road conditions. To ensure maximum model performance, the model is optimized with the Adam optimizer and includes training using sparse stratified cross-entropy loss. Furthermore, Checkpoint model calls are carefully used to protect the best models during the training process. The overall objective of this research project is to provide an efficient and accurate real-time solution for collision detection and the ultimate goal is to make a significant contribution. This will lead to improved traffic safety. This has the potential not only to prevent accidents, but also to reduce their severity, thereby significantly improving the safety and efficiency of transportation and ultimately improving the overall well-being of society.
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Cui, Shunfeng, Yiyang Chen, and Xinlin Li. "A Robust and Efficient UAV Path Planning Approach for Tracking Agile Targets in Complex Environments." Machines 10, no. 10 (2022): 931. http://dx.doi.org/10.3390/machines10100931.
Full textAbstract:
The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose a robust and efficient UAV path planning approach for tracking agile targets aggressively and safely. This approach comprehensively takes into account the historical observations of the tracking target and the surrounding environment of the location. It reliably predicts a short time horizon position of the moving target with respect to the dynamic constraints. Firstly, via leveraging the Bernstein basis polynomial and combining obstacle distribution information around the target, the prediction module evaluated the future movement of the target, presuming that it endeavored to stay away from the obstacles. Then, a target-informed dynamic searching method was embraced as the front end, which heuristically searched for a safe tracking trajectory. Secondly, the back-end optimizer ameliorated it into a spatial–temporal optimal and collision-free trajectory. Finally, the tracking trajectory planner generated smooth, dynamically feasible, and collision-free polynomial trajectories in milliseconds, which is consequently reasonable for online target tracking with a restricted detecting range. Statistical analysis, simulation, and benchmark comparisons show that the proposed method has at least 40% superior accuracy compared to the leading methods in the field and advanced capabilities for tracking agile targets.
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Alabdalbari, Ayad Abdulrahem, and Issa Ahmed Abed. "New robot path planning optimization using hybrid GWO-PSO algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1289–96. http://dx.doi.org/10.11591/eei.v11i3.3677.
Full textAbstract:
Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and follows path perfection guidelines, the primary shape is improved. In addition, simulation findings reveal that the proposed HGWO-PSO method is deeply serious in terms of path optimality when compared to path planning approaches such as group search optimizer GSO, PSO, artificial bee colony ABC, and GWO.
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Ayad, Abdulrahem Alabdalbari, and Ahmed Abed Issa. "New robot path planning optimization using hybrid GWO-PSO algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1289~1296. https://doi.org/10.11591/eei.v11i3.3677.
Full textAbstract:
Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and follows path perfection guidelines, the primary shape is improved. In addition, simulation findings reveal that the proposed HGWOPSO method is deeply serious in terms of path optimality when compared to path planning approaches such as group search optimizer GSO, PSO, artificial bee colony ABC, and GWO.
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Herrmann, Lukas, Roland Boumann, Mario Lehmann, Samuel Müller, and Tobias Bruckmann. "Simulation-Based Comparison of Novel Automated Construction Systems." Robotics 11, no. 6 (2022): 119. http://dx.doi.org/10.3390/robotics11060119.
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As automated construction processes require large workspaces and high payloads, the use of cables is a reasonable approach to span wide distances and share loads. Therefore, a cable-driven parallel robot is a suitable choice for automated masonry construction. Another possible robotic system for this task consists of a set of cooperative drones, each connected to the end effector and the payload by a cable. Because of the similarities between the two robotic systems, the same object-oriented programmed software can be used for trajectory planning and subsequent investigations, making minor adjustments. The implemented optimizing path planning algorithm takes into account the physical boundaries, motion time, collision avoidance and energy requirements. Thus, a simulation-based comparison of the characteristics of both systems can be made. In this paper, the necessary physical models for both the drone system and the cable robot are derived in detail. Based on the common framework, this paper presents a comparison between the two robotic systems, defining two different scenarios. The first scenario demonstrates the functioning of the optimizer approach. The second scenario is used to compare the two systems. For this purpose, the trajectories for all 1720 masonry units of the first floor of a house are optimized. The analysis of the results shows that both systems can transport heavy loads, with the cable robot having advantages on smaller sites, while the drone system covers larger distances for the price of slower performance and higher energy consumption.
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Hongfan, Gui, and Zhao Zhangyan. "Research on obstacle climbing gait structure design and gait control of hexapod wall climbing robot based on STM32F103 core controller." Mechanics & Industry 24 (2023): 20. http://dx.doi.org/10.1051/meca/2023019.
Full textAbstract:
The hexapod wall climbing robots have the advantages of traversing complex wall surfaces. To traverse complex environments autonomously, it must possess the capability to select gait parameters and paths appropriate for the wall surface. Path planning and gait optimization is a fundamental issue in the aspect of stable, energy efficient robot navigation in complex environments with static and dynamic obstacles. Traditional statistical models have been developed to get the optimal path and gait parameters but the result obtained was very poor. Metaheuristic algorithms are gaining importance in robotic gait planning. In this paper, we proposed robust two stage gait planning approach for predicting collision-free, distance-minimal, smooth navigation path and ensuring stable, energy efficient gait patterns for robots using hybrid metaheuristic algorithms. In the first stage, optimal climbing path for robot is predicted using Tri-objective Grey Wolf Path Optimization (TGWPO) based on obstacle and target detection. In the second stage, the gait parameters adaptive to the constructed climbing path are optimized using Adaptive multi-objective Particle swarm optimization (AMPSO). The hexapod wall climbing robot is designed with STM32F103 as core controller modeled with optimal path planner (using TDWPO) and gait optimizer module (using AMPSO). STM32F103 controller commands and controls the robot to climb on wall with optimized gait parameters according to the optimal path. We analyzed the efficacy of the proposed two stage gait planning approach using TDWPO-AMPSO for hexapod wall climbing robots with existing gait planning approaches in terms of path length, climbing time, gait stability, obstacle avoidance, and energy efficiency. The result analysis showed that the suggested gait planning approach is efficient over conventional strategies for climbing robots.
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Xu, Qingwei, Xiangyang Lu, and Juncai Xu. "Optimized Active Collision Avoidance Algorithm of Intelligent Vehicles." Electronics 12, no. 11 (2023): 2451. http://dx.doi.org/10.3390/electronics12112451.
Full textAbstract:
This research introduces an innovative strategy to impede and lessen lateral and rear-end vehicular collisions by consolidating braking systems with active emergency steering controls. This study puts forward a T-type active emergency steering method, designed to circumvent both lateral and rear-end collisions at vehicular intersections. To secure vehicular stability and condense the time required for steering during the T-type active emergency process, this research formulates a nonlinear dynamic model for the vehicle, in addition to a nonlinear tire model. This study also engages in a thorough analysis of the constraints linked to the initial and terminal states of the steering process. The issue at hand is articulated as an optimization control problem with boundary value restrictions, which is subsequently resolved using the Radau pseudospectral method. Simulation results corroborate that the prompt commencement of the anti-collision strategy can effectively deter potential collisions. This pioneering approach shows considerable promise in augmenting the active safety of intelligent vehicles and bears meaningful implications for high-precision automotive collision evasion systems.
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Li, Weifeng, Lufeng Zhong, Yaochen Liu, and Guoyou Shi. "Ship Intrusion Collision Risk Model Based on a Dynamic Elliptical Domain." Journal of Marine Science and Engineering 11, no. 6 (2023): 1122. http://dx.doi.org/10.3390/jmse11061122.
Full textAbstract:
To improve navigation safety in maritime environments, a key step is to reduce the influence of human factors on the risk assessment of ship collisions by automating the decision-making process as much as possible. This paper optimizes a dynamic elliptical ship domain based on Automatic Identification System (AIS) data, combines the relative motion between ships in different encounter situations and the level of ship intrusion in the domain, and proposes a ship intrusion collision risk (SICR) model. The simulation results show that the optimized ship domain meets the visualization requirements, and the intrusion model has good collision risk perception ability, which can be used as the evaluation standard of ship collision risk: when the SICR is 0.5–0.6, the ship can establish a collaborative collision avoidance decision-making relationship with other ships, and the action ship can take effective collision avoidance action at the best time when the SICR is between 0.3 and 0.5. The SICR model can give navigators a more accurate and rapid perception of navigation risks, enabling timely maneuvering decisions, and improving navigation safety.
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Zhu, Hongyang, and Yi Ding. "Optimized Dynamic Collision Avoidance Algorithm for USV Path Planning." Sensors 23, no. 9 (2023): 4567. http://dx.doi.org/10.3390/s23094567.
Full textAbstract:
Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. The approach combines a model that accounts for the two degrees of freedom in USV dynamics with a velocity obstacle method for obstacle detection and avoidance. The method calculates the change in the USV’s navigation state based on the critical condition of collision avoidance. First, the coordinates of the optimal collision avoidance point in the current ship encounter state are calculated based on the relative velocities and kinematic parameters of the USV and obstacles. Then, the increments of the vessel’s linear velocity and heading angle that can reach the optimal collision avoidance point are set as a constraint for dynamic window sampling. Finally, the algorithm evaluates the probabilities of collision hazards for trajectories that satisfy the critical condition and uses the resulting collision avoidance probability value as a criterion for course assessment. The resulting collision avoidance algorithm is optimized for USV maneuverability and is capable of handling multiple moving obstacles in real-time. Experimental results show that the OCAP algorithm has higher and more robust path-finding efficiency than the other two algorithms when the dynamic obstacle density is higher.
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Li, Fangda, Ankit Manerikar, and Avinash Kak. "RMPD — A Recursive Mid-Point Displacement Algorithm for Path Planning." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 468–75. http://dx.doi.org/10.1609/icaps.v28i1.13921.
Full textAbstract:
Motivated by what is required for real-time path planning, the paper starts out by presenting RMPD, a new recursive ''local'' planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of RMPD by introducing the notion of cost-awareness into the algorithm to improve the path quality -- this is done by associating obstacle and smoothness costs with the currently selected path points and factoring those costs in choosing the best points for the next iteration. In this manner, the overall strategy in the cost-aware form of RMPD, cRMPD, combines the computational efficiency made possible by the recursive RMPD planner with the cost efficacy of a stochastic trajectory optimizer to rapidly produce high-quality local collision-free paths. Based on the test cases we have run, our experiments show that cRMPD can reduce planning time by up to two orders of magnitude as compared to RRT-Connect, while still maintaining a path length optimality equivalent to that of RRT*.
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Zhang, Qinglei, Jing Hu, Zhen Liu, and Jianguo Duan. "Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing." PLOS ONE 19, no. 1 (2024): e0297139. http://dx.doi.org/10.1371/journal.pone.0297139.
Full textAbstract:
In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.
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Liu, Xin, Shuwei Ren, Lei Zhang, Wei Shen, and Yubo Tu. "Research on Dynamic Path Planning and Tracking Control for Ship Collision Avoidance." Journal of Physics: Conference Series 2607, no. 1 (2023): 012012. http://dx.doi.org/10.1088/1742-6596/2607/1/012012.
Full textAbstract:
Abstract Ship collisions are prevalent every year, leading to significant maritime traffic accidents. This paper presents research on dynamic path planning and tracking control for ship collision avoidance by integrating ship automatic avoidance technology to address this issue. We conducted a comprehensive study on artificial potential fields, trajectory tracking, and route trajectory tracking in response to the current state of ship collision avoidance and trajectory tracking. The study employed vector decomposition and slider control as research methods to analyze, optimize, and modify ship collision avoidance methods. Additionally, we carried out collision avoidance simulations using MATLAB to verify the stability and safety of ship trajectory tracking under various methods to advance the research on ship collision avoidance and trajectory. The proposed approach has the potential to significantly reduce ship collisions and enhance ship trajectory safety.
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Li, Jinxin, Hongbo Wang, Wei Zhao, and Yuanyuan Xue. "Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance." Journal of Advanced Transportation 2019 (April 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/4068783.
Full textAbstract:
With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.
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Wu, Xiaolie, Kezhong Liu, Jinfen Zhang, Zhitao Yuan, Jiongjiong Liu, and Qing Yu. "An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations." Journal of Advanced Transportation 2021 (August 27, 2021): 1–17. http://dx.doi.org/10.1155/2021/7537825.
Full textAbstract:
Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.
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Latif, Shaikh Abdul, Ibrahim M. Mehedi, Ahmed I. M. Iskanderani, Mahendiran T. Vellingiri, and Rahtul Jannat. "Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission." Computational Intelligence and Neuroscience 2022 (June 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/4698936.
Full textAbstract:
This manuscript proposes a hybrid method for landing trajectory generation of unmanned lunar mission. The proposed hybrid control scheme is the joint execution of the human urbanization algorithm (HUA) and political optimizer (PO) with radial basis functional neural network (RBFNN); hence it is named as HUA-PORFNN method. The HUA is a metaheuristic method, and it is used to solve several optimization issues and several nature-inspired methods to enhance the convergence speed with quality. On the other hand, multiple-phased political processes inspire the PO. The work aims to guide the lander with minimal fuel consumption from the initial to the final stage, thus minimizing the lunar soft landing issues based on the given cost of operation. Here, the HUAPO method is implemented to overcome thrust discontinuities, checkpoint constraints are suggested for connecting multi-landing phases, angular attitude rate is modeled to obtain radical change rid, and safeguards are enforced to deflect collision along with obstacles. Moreover, first, the issues have been resolved according to the proposed HUAPO method. Here, energy trajectories with 3 terminal processes are deemed. Additionally, the proposed HUAPO method is executed on MATLAB/Simulink site, and the performance of the proposed method is compared with other methods.
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Xu, Peng, and Qingyun Sun. "Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm." Applied Sciences 13, no. 11 (2023): 6366. http://dx.doi.org/10.3390/app13116366.
Full textAbstract:
In order to improve the performance in terms of detecting objects colliding in virtual reality, the ant colony algorithm was used to detect collisions. In the preliminary detection stage, the OBB bounding box and the spherical bounding box were used to detect the collision of objects, and the objects that may collide were selected. In the accurate detection stage, the model was sampled, and the feature pairs were used as the set to be detected for detecting collisions, the collision detection problem of the three-dimensional model was transformed into a nonlinear optimization problem of the distance between the feature pairs in the two-dimensional discrete space. The ant colony algorithm was introduced to solve the problem, and the pheromone concentration and update rules of the ant colony algorithm were optimized to improve the efficiency of the algorithm. The simulation results showed that, compared with the commonly used collision detection algorithms, our algorithm had high accuracy in detecting collisions and was less time-consuming.
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Mohamed, Abdulrahman. "Novel approach for anti-collision planning optimization in directional wells." International Journal of Engineering & Technology 9, no. 2 (2020): 333. http://dx.doi.org/10.14419/ijet.v9i2.30306.
Full textAbstract:
One of the most application of the directional drilling is drilling multiple wells from one location or platform. In drilling multiple wells from one location the major problem that faced is avoiding the collision with the offset wells that drilled near the proposed well in the same region. Therefore, the Potential of Collison between the wells can cause severe catastrophic accidents such as an explosion or oil spill. Several measurements of proximity calculation or methods have been adopted to control the distance between the wells, avoid the Collison, increas-ing the clearance along with smoothing the trajectory, Reducing the drilling time based on the anti-collision rules. A real case study of an offshore directional horizontal well drilled from the platform is studied through the paper. The proposed well is drilled in the neighboring of three Offset wells that should be Planned completely to avoid the Collison with them. The well is planned through an advanced anti-collision method that results in preventing the collision of well with optimized drilling performance through Oriented separation factor (OSF). This factor yields appropriate separation with OSF greater than 5. This yield efficient separation with offset well 1, offset well 2 and offset well 3 greater thant5, In addition to optimized drilling performance of 84% drilling versus 16% sliding that results in the completion of the well in 50 days with positive income that result in 8.55 Return on Investment (ROI).
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Jiang, Xin. "Research on Coarse-Grained Discrete Element Model and Optimization for Fine Particles." Coatings 12, no. 10 (2022): 1483. http://dx.doi.org/10.3390/coatings12101483.
Full textAbstract:
Optimization is important for the performance improvement of mechanical equipment. To advance this approach, a coarse-grained model for the discrete element method (DEM) is proposed with consideration of mechanical structure. This study identified a coarse-grained model that can be used in particle simulation, and designed a mixing equipment model, which was further optimized through combination with the coarse-grained model. The optimization and characteristics of a stirred mill were investigated. The novelty of this study is that the coarse-grained model was used for equipment optimization. Different results were obtained for different model structures. Concentration is related to the model. The average collision energy was obtained from media-to-wall or particle-to-wall collisions. The largest number of collisions that cause different string performance in different models was obtained. The optimized model had the largest average collision energy. The characteristics of different models combined with the coarse-grained model were determined, and useful results regarding the collision energy were obtained for future performance considerations. In summary, a suitable model was established and combined with an appropriate coarse-grained model to achieve performance improvement.
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Wang, Danqi, Wengang Deng, Lintao Wu, Li Xin, Lizhe Xie, and Honghao Zhang. "Impact of Vehicle Steering Strategy on the Severity of Pedestrian Head Injury." Biomimetics 9, no. 10 (2024): 593. http://dx.doi.org/10.3390/biomimetics9100593.
Full textAbstract:
In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles’ driving status to make emergency evasive decisions. Thus, the collision simulation models for pedestrians and three types of vehicles, i.e., sedans, Sport Utility Vehicles (SUVs), and Multi-Purpose Vehicle (MPVs), are built to investigate the impact of vehicle types, vehicle steering angles, collision speeds, collision positions, and pedestrian orientations on head injuries of pedestrians. The results indicate that the Head Injury Criterion (HIC) value of the head increases with the increase in collision speed. Regarding the steering angles, when a vehicle’s steering direction aligns with a pedestrian’s position, the pedestrian remains on top of the vehicle’s hood for a longer period and moves together with the vehicle after the collision. This effectively reduces head injuries to pedestrians. However, when the vehicle’s steering direction is opposite to the pedestrian’s position, the pedestrian directly collides with the ground, resulting in higher head injuries. Among them, MPVs cause the most severe injuries, followed by SUVs, and sedans have the least impact. Overall, intelligent vehicles have great potential to reduce head injuries of pedestrians in the event of sudden pedestrian-vehicle collisions by combining with Automatic Emergency Steering (AES) measures. In the future, efforts need to be made to establish an optimized steering strategy and optimize the handling of situations where steering is ineffective or even harmful.
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Yue, Xuezhi, Ye Fan, Yuan Zeng, Weitao Fan, and Luhui Zhou. "An Improved Fast Collision Detection Algorithm for Human Models Based on Hybrid Bounding Boxes." International Journal of Cognitive Informatics and Natural Intelligence 18, no. 1 (2024): 1–13. http://dx.doi.org/10.4018/ijcini.345655.
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This article analyzes the commonly used collision detection methods in game engines and designs a hybrid bounding box structure that is more suitable for human models based on their motion characteristics. In addition, this article also optimized the collision response algorithm after the system detects collisions, making the collision response process faster. Through experimental analysis, this approach has a good effect in addressing the problem of model penetration caused by continuously changing the model posture in shooting games; it also avoids the game fairness problem caused by model penetration and improves the realism of the game's virtual environment.
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Chen, Baolong, Jianping Wang, Jiahao Zhou, et al. "Collision Probability Evaluation of LAMOST Robotic Fiber Positioners in the Design Phase." Astronomical Journal 170, no. 1 (2025): 36. https://doi.org/10.3847/1538-3881/adda42.
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Abstract Multiobject spectroscopic telescopes are crucial for modern astronomy. Among them, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), with thousands of robotic fiber positioners (RFPs), is the one with the highest spectra acquisition rates. To guarantee no blind area, RFPs’ working spaces are overlapped to a certain extent, leading to collisions between RFPs and damage to RFPs. Even with algorithms designed for collision avoidance, collisions still occur during operation. Despite a 99.6% success rate of the current adopted path-planning algorithm, 4% of RFPs fail to reach targets. Fail reasons, such as collisions, cause ongoing negative effects across multiple observation rounds, necessitating the replacement of many RFPs. LAMOST is upgrading its fiber positioning system. Considering the above-mentioned problem in the design phase is necessary. To address this challenge, we propose a mathematical model to assess the collision probability. Then, the proposed model is validated by Monte Carlo simulations. During collision probability calculation, we consider factors such as RFP structure, target allocation, motion requirements, and mechanical errors into consideration. Based on this, we employ the genetic algorithm to optimize RFP arrangements with the lowest collision probability and show its function in the design phase. The proposed method is designed for LAMOST, but it is suitable for the future design of newly proposed spectroscopic telescopes with RFPs.
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Li, Qiang. "A Research on Autonomous Collision Avoidance under the Constraint of COLREGs." Sustainability 15, no. 3 (2023): 2446. http://dx.doi.org/10.3390/su15032446.
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In this paper, a decision-making model suitable for the collision avoidance (CA) of numerous target ships (TSs) is proposed, based on the principle of ship collision avoidance geometry and the characteristics of numerous target ships’ collision avoidance at sea. To ensure that the collision avoidance behaviors of own-ship (OS) are subject to the International Regulations for Preventing Collisions at Sea (COLREGS), this paper gives full consideration to the requirements of COLREGS within the scope of CA action and the time of collision avoidance. A ship CA simulation is established based on the Mathematical Modeling Group (MMG) model. To optimize the CA decision-making model, the influence of hydrodynamic force on steering time required to reach the new course is integrated into the collision avoidance simulation system. The simulation results show that the method can quickly and effectively determine a collision avoidance decision under the complex situation of numerous target ships and static obstacles, and it can consider the unpredictable strategies used by other vessels.
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Halonen, Roope, Evgeni Zapadinsky, Theo Kurtén, Hanna Vehkamäki, and Bernhard Reischl. "Rate enhancement in collisions of sulfuric acid molecules due to long-range intermolecular forces." Atmospheric Chemistry and Physics 19, no. 21 (2019): 13355–66. http://dx.doi.org/10.5194/acp-19-13355-2019.
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Abstract. Collisions of molecules and clusters play a key role in determining the rate of atmospheric new particle formation and growth. Traditionally the statistics of these collisions are taken from kinetic gas theory assuming spherical noninteracting particles, which may significantly underestimate the collision coefficients for most atmospherically relevant molecules. Such systematic errors in predicted new particle formation rates will also affect large-scale climate models. We studied the statistics of collisions of sulfuric acid molecules in a vacuum using atomistic molecular dynamics simulations. We found that the effective collision cross section of the H2SO4 molecule, as described by an optimized potentials for liquid simulation (OPLS). OPLS all-atom force field, is significantly larger than the hard-sphere diameter assigned to the molecule based on the liquid density of sulfuric acid. As a consequence, the actual collision coefficient is enhanced by a factor of 2.2 at 300 K compared with kinetic gas theory. This enhancement factor obtained from atomistic simulation is consistent with the discrepancy observed between experimental formation rates of clusters containing sulfuric acid and calculated formation rates using hard-sphere kinetics. We find reasonable agreement with an enhancement factor calculated from the Langevin model of capture, based on the attractive part of the atomistic intermolecular potential of mean force.
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Li, Jinxin, Hongbo Wang, Zhiying Guan, and Chong Pan. "Distributed Multi-Objective Algorithm for Preventing Multi-Ship Collisions at Sea." Journal of Navigation 73, no. 5 (2020): 971–90. http://dx.doi.org/10.1017/s0373463320000053.
Full textAbstract:
Avoidance of collisions at sea is crucial to navigational safety. In this paper, we use a distributed algorithm to communicate the entire collision avoidance trajectory information for each ship. In each communication, we suggest a new improvement function considering safety and efficiency to identify the avoidance ship in each cycle. Considering the nonlinear collision avoidance trajectory of ships, a new method for calculating the degree of danger using a velocity obstacle algorithm is proposed. Therefore, in each communication, each ship considers the avoidance behaviours of other ships in planning its avoidance trajectory. Additionally, we combine bi-criterion evolution (BCE) and the ant lion optimiser to plan the entire collision avoidance path. Three scenarios are designed to demonstrate the performance of this method. The results show that the proposed method can find a suitable collision-free solution for all ships.