Artículos de revistas: "Adaptivive trajectory planning"

1

Wang, Xin, Junwei Wang y Zhi Rao. "An adaptive parametric interpolator for trajectory planning". Advances in Engineering Software 41, n.º 2 (febrero de 2010): 180–87. http://dx.doi.org/10.1016/j.advengsoft.2009.09.010.

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2

Wang, Yuan, Zhenglei Wei, Changqiang Huang, Hanqiao Huang, Kexin Zhao y Cong Li. "Online Cooperative Trajectory Planning for UCAV Formation in Uncertain Environment". Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, n.º 6 (diciembre de 2018): 1145–55. http://dx.doi.org/10.1051/jnwpu/20183661145.

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This paper presents a local online planning method based on hp adaptive pseudospectral method to address the cooperative trajectory planning of UCAV formation in uncertain environment. First, through analyzing attacking process of UCAV formation, the cooperative trajectory planning model is built up by taking UCAV threat, task time and the number of collision and the error of ordered time as the object function. Second, in order to solve the online cooperative trajectory planning, according to real-time environment information and offline planning data, the local planning method based on hp adaptive pseudospectral method is proposed during optimizing time slice. Last but not least, the simulation results for offline and online cooperative trajectory show that the proposed method is valid and can provide cooperative trajectory with high precise control and state information.

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3

Vu, Nga Thi-Thuy, Nam Phuong Tran y Nam Hoai Nguyen. "Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm". Journal of Robotics 2018 (2 de diciembre de 2018): 1–7. http://dx.doi.org/10.1155/2018/2571243.

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This paper presents a scheme based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.

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4

Kim, Kijung, Youngsoo Kim, Jongwon Kim, Hwa Soo Kim y Taewon Seo. "Optimal Trajectory Planning for 2-DOF Adaptive Transformable Wheel". IEEE Access 8 (2020): 14452–59. http://dx.doi.org/10.1109/access.2020.2966767.

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5

Shin, Jin-Ho y Ju-Jang Lee. "Trajectory planning and robust adaptive control for underactuated manipulators". Electronics Letters 34, n.º 17 (1998): 1705. http://dx.doi.org/10.1049/el:19981191.

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6

Wei, Zhenglei, Changqiang Huang, Dali Ding, Hanqiao Huang y Huan Zhou. "UCAV Formation Online Collaborative Trajectory Planning Using hp Adaptive Pseudospectral Method". Mathematical Problems in Engineering 2018 (22 de octubre de 2018): 1–25. http://dx.doi.org/10.1155/2018/3719762.

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In this paper, a novel approach to solving the formation online collaborative trajectory planning for fixed-wing Unmanned Combat Aerial Vehicles (UCAVs) is proposed. In order to describe the problem, the formation attack process which consists of communication framework and synergy elements is analyzed. The collaborative trajectory planning model which is based on avoiding the threat zones, reducing the execution time, and accomplishing the mission combines kinematics/dynamics model of UCAV with formation relative motion model to establish the optimal control problem. The approach based on hp adaptive pseudospectral method is presented to generate formation trajectory that satisfies the collaborative constraints. When a trigger event is detected, based on the offline planning, the online collaborative trajectory replanning using rolling horizon strategy is carried out. Simulated experiments which are divided into offline scenarios and online scenarios demonstrate that the proposed approach can generate trajectories which can meet the actual flight constraints, and the results verify the feasibility and stability of the proposed approach.

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7

ZHANG, HE, RUI WU, CHANGLE LI, XIZHE ZANG, YANHE ZHU, HONGZHE JIN, XUEHE ZHANG y JIE ZHAO. "ADAPTIVE MOTION PLANNING FOR HITCR-II HEXAPOD ROBOT". Journal of Mechanics in Medicine and Biology 17, n.º 07 (noviembre de 2017): 1740040. http://dx.doi.org/10.1142/s0219519417400401.

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Multi-legged robots have the ability to traverse rugged terrain and can surmount the obstacles, which are impossible for being overcome by wheeled robots. In this regard, six-legged (hexapod) robots are considered to provide the best combination of adequate adaptability and control complexity. Their motion planning envisages calculating sequences of footsteps and body posture, accounting for the influence of terrain shape, in order to produce the appropriate foot-end trajectory and ensure stable and flexible motion of hexapod robots on the rugged terrain. In this study, a high-order polynomial is used to describe the trajectory model, and a new motion planning theory is proposed, which is aimed at the adaptation of hexapod robots to more complex terrains. An attempt is made to elaborate the adaptive motion planning and perform its experimental verification for a novel hexapod robot HITCR-II, demonstrating its applicability for walking on the unstructured terrain.

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8

Marti, K. "Stochastic Programming Methods in Adaptive Optimal Trajectory Planning for Robots". ZAMM 82, n.º 11-12 (noviembre de 2002): 795–809. http://dx.doi.org/10.1002/1521-4001(200211)82:11/12<795::aid-zamm795>3.0.co;2-i.

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9

Bureerat, Sujin, Nantiwat Pholdee, Thana Radpukdee y Papot Jaroenapibal. "Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning". Expert Systems with Applications 136 (diciembre de 2019): 133–44. http://dx.doi.org/10.1016/j.eswa.2019.06.033.

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10

Kim, Junsoo, Kichun Jo, Wonteak Lim y Myoungho Sunwoo. "A probabilistic optimization approach for motion planning of autonomous vehicles". Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, n.º 5 (16 de agosto de 2017): 632–50. http://dx.doi.org/10.1177/0954407017704782.

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This paper presents a novel probabilistic approach for improving the motion planning performance of autonomous driving. The proposed approach is based on the sampling-based planning algorithm, which generates an optimal trajectory from a set of trajectory candidates. In order to treat the uncertainty in the perception data and the vehicle system, the particle filter framework is applied to the motion planning algorithm with four main steps: the time update of the trajectory candidates, the perception measurement update, the trajectory selection and the motion goal resampling. Since the proposed planning algorithm recursively generates an optimal trajectory, the time update of the trajectory candidate updates the motion goals of the trajectory candidates in the previous step using the vehicle model, and it also generates a new set of candidates. In order to evaluate the optimality of each candidate with regard to the safety and the reliability, a perception measurement update is performed. In this step, the importance weight of each candidate is computed using perception data and its adaptive likelihood function. Based on the candidates with updated importance weights, an optimal trajectory is determined in the trajectory selection. Then, the motion goal resampling modifies the set of motion goals based on the importance weights for efficient management of the motion goals in the iterative planning algorithm. The developed algorithm is validated using various types of test. The results show that the proposed method not only provides an integrated probabilistic interface between the perception and the planning but also results in an excellent performance in terms of the computation efficiency.

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11

Jiao, Sheng-xi, Hao Wang, Lin-lin Xia y Shuai Zhang. "Research on Trajectory Planning of 6-DOF Cutting-robot in Machining Complex Surface". MATEC Web of Conferences 220 (2018): 06003. http://dx.doi.org/10.1051/matecconf/201822006003.

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It is important and difficult for the complicated surface processing in mechanical industry. In this paper, an improved algorithm for trajectory planning is proposed in impeller surface processing by using 6-DOF cutting-robot. Taking a single finished path of the impeller blade as an example, the feedrate of the cutter, bow height error, cutter-orientation and position are planned by the B-spline interpolation algorithm, the best cutting trajectory is obtained. On the basis of trajectory planning, the optimal movement scheme of 6-DOF cutting-robot joints is obtained, the 6-DOF cutting-robot feedrate and trajectory smooth transition is achieved and the joints movement adaptive adjustment is completed. Finally, the angles, the angular velocitys of the joints and their interrelated properties are analyzed. The research works indicate that the robot joint angle curves are continuous and stable, which has met the requirements of smooth movement of the robot, and the results show that the trajectory planning is effective and practical.

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12

Qu, S. y K. Marti. "Adaptive Stochastic Trajectory Planning For Robots - Numerical Results For Manutec r3-". ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 78, S3 (1998): 1041–42. http://dx.doi.org/10.1002/zamm.19980781589.

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13

Zhou, Haibo, Shun Zhou, Jia Yu, Zhongdang Zhang y Zhenzhong Liu. "Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method". Applied Sciences 10, n.º 3 (31 de enero de 2020): 935. http://dx.doi.org/10.3390/app10030935.

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In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

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14

Song, Qisong, Shaobo Li, Qiang Bai, Jing Yang, Ansi Zhang, Xingxing Zhang y Longxuan Zhe. "Trajectory Planning of Robot Manipulator Based on RBF Neural Network". Entropy 23, n.º 9 (13 de septiembre de 2021): 1207. http://dx.doi.org/10.3390/e23091207.

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Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

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15

Ćurković, Petar, Bojan Jerbić y Tomislav Stipančić. "Swarm-Based Approach to Path Planning Using Honey-Bees Mating Algorithm and ART Neural Network". Solid State Phenomena 147-149 (enero de 2009): 74–79. http://dx.doi.org/10.4028/www.scientific.net/ssp.147-149.74.

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In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.

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16

Shi, Junren, Dongye Sun, Datong Qin, Minghui Hu, Yingzhe Kan, Ke Ma y Ruibo Chen. "Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control". Robotics and Autonomous Systems 131 (septiembre de 2020): 103570. http://dx.doi.org/10.1016/j.robot.2020.103570.

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17

Liu, Tong, Guo-fang Gong, Hua-yong Yang, Yu-xi Chen y Yi Zhu. "Trajectory Control of Tunnel Boring Machine Based on Adaptive Rectification Trajectory Planning and Multi-cylinders Coordinated Control". International Journal of Precision Engineering and Manufacturing 20, n.º 10 (19 de agosto de 2019): 1721–33. http://dx.doi.org/10.1007/s12541-019-00073-5.

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18

Azadi, Sassan. "Utilizing an Adaptive Controller (Azadi Controller) for Trajectory Planning of PUMA 560 Robot". Advanced Materials Research 403-408 (noviembre de 2011): 4880–87. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4880.

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This research work was devoted to present a novel adaptive controller which uses two negative stable feedbacks with a positive unstable positive feedback. The positive feedback causes the plant to do the break, therefore reaching the desired trajectory with tiny overshoots. However, the two other negative feedback gains controls the plant in two other sides of positive feedback, making the system to be stable, and controlling the steady-state, and transient responses. This controller was performed for PUMA-560 trajectory planning, and a comparison was made with a fuzzy controller. The fuzzy controller parameters were obtained according to the PSO technique. The simulation results shows that the novel adaptive controller, having just three parameters, can perform well, and can be a good substitute for many other controllers for complex systems such as robotic path planning.

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19

Skrimizea, Eirini, Helene Haniotou y Constanza Parra. "On the ‘complexity turn’ in planning: An adaptive rationale to navigate spaces and times of uncertainty". Planning Theory 18, n.º 1 (7 de junio de 2018): 122–42. http://dx.doi.org/10.1177/1473095218780515.

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Complexity sciences have been long ago acknowledged to be useful at conceptualizing a variety of phenomena relevant to planning. Nevertheless, the actual mechanisms that will prove adequate to tackle complex planning issues are still under debate. Considering that in today’s so-called era of the Anthropocene such planning issues are more present and evident than ever, the need for further investigating the implications of complexity sciences into building planning approaches becomes very relevant. In this article, we use the concept of complex systems as an analytical framework challenging our understanding of planning and we argue in favour of a ‘complexity turn’ in planning through the adaptive rationale. We define the adaptive rationale as an additional, both normative and analytical, trajectory in planning theory, in the interplay between certainty and uncertainty. Finally, to assimilate this rationale into planning mechanisms capable to respond to contemporary social and ecological challenges, we call for issue-driven adaptive planning approaches conceptualized through normative sustainability and nourished by post-normal science.

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20

Aurnhammer, A. y K. Marti. "Adaptive Optimal Stochastic Trajectory Planning in real-time using neural network approximations". ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 81, S3 (2001): 653–54. http://dx.doi.org/10.1002/zamm.200108115102.

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21

Mohan, Rishi, Emilia Silvas, Henry Stoutjesdijk, Herman Bruyninckx y Bram De Jager. "Collision-Free Trajectory Planning With Deadlock Prevention: An Adaptive Virtual Target Approach". IEEE Access 8 (2020): 115240–50. http://dx.doi.org/10.1109/access.2020.3004205.

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22

Sun, Xin, Senchun Chai y Baihai Zhang. "Trajectory Planning of the Unmanned Aerial Vehicles with Adaptive Convex Optimization Method". IFAC-PapersOnLine 52, n.º 12 (2019): 67–72. http://dx.doi.org/10.1016/j.ifacol.2019.11.071.

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23

Zhang, Xiaolong, Yu Huang, Youmin Rong, Gen Li, Hui Wang y Chao Liu. "Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints". Sensors 21, n.º 2 (6 de enero de 2021): 335. http://dx.doi.org/10.3390/s21020335.

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With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

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Kang, Liang y Lian Cheng Mao. "Path Planning for Nonholonomic Mobile Robot in Dynamic Environment". Applied Mechanics and Materials 462-463 (noviembre de 2013): 771–74. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.771.

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Based on introduction of the fluid diffusion energy, the model for path planning is presented. The adaptive mesh is used to solve the equation model for path planning. Based on the dynamic model and kinematic constraints of the nonholonomic mobile robot, a trajectory tracking controller is designed. Theory and calculation results prove that, as a new method for mobile robot path planning, the equation of the fluid diffusion energy for nonholonomic mobile robot path planning is feasible and effective.

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Gou, Zhi Jian. "The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot". Applied Mechanics and Materials 716-717 (diciembre de 2014): 1555–58. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1555.

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The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

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Gou, Zhi Jian. "The Research of Timing-Optimal Trajectory Planning Based on Improved Genetic Algorithms". Applied Mechanics and Materials 433-435 (octubre de 2013): 562–65. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.562.

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The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. Program the operation process in the MATLAB software. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained by the improved genetic algorithms under the kinematical constraints.

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27

Lin, Fen, Kaizheng Wang, Youqun Zhao y Shaobo Wang. "Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction". Sensors 20, n.º 4 (17 de febrero de 2020): 1079. http://dx.doi.org/10.3390/s20041079.

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An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

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Meng, Yiping, Yiming Sun y Wen-shao Chang. "Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm". Construction Robotics 5, n.º 2 (11 de abril de 2021): 131–46. http://dx.doi.org/10.1007/s41693-021-00057-w.

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AbstractIn this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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Wang, Chaofeng, Li Wei, Zhaohui Wang, Min Song y Nina Mahmoudian. "Reinforcement Learning-Based Multi-AUV Adaptive Trajectory Planning for Under-Ice Field Estimation". Sensors 18, n.º 11 (9 de noviembre de 2018): 3859. http://dx.doi.org/10.3390/s18113859.

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This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters.

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Gan, Yahui, Jinjun Duan, Ming Chen y Xianzhong Dai. "Multi-Robot Trajectory Planning and Position/Force Coordination Control in Complex Welding Tasks". Applied Sciences 9, n.º 5 (5 de marzo de 2019): 924. http://dx.doi.org/10.3390/app9050924.

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In this paper, the trajectory planning and position/force coordination control of multi-robot systems during the welding process are discussed. Trajectory planning is the basis of the position/ force cooperative control, an object-oriented hierarchical planning control strategy is adopted firstly, which has the ability to solve the problem of complex coordinate transformation, welding process requirement and constraints, etc. Furthermore, a new symmetrical internal and external adaptive variable impedance control is proposed for position/force tracking of multi-robot cooperative manipulators. Based on this control approach, the multi-robot cooperative manipulator is able to track a dynamic desired force and compensate for the unknown trajectory deviations, which result from external disturbances and calibration errors. In the end, the developed control scheme is experimentally tested on a multi-robot setup which is composed of three ESTUN industrial manipulators by welding a pipe-contact-pipe object. The simulations and experimental results are strongly proved that the proposed approach can finish the welding task smoothly and achieve a good position/force tracking performance.

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Scheidt, Robert A. y Claude Ghez. "Separate Adaptive Mechanisms for Controlling Trajectory and Final Position in Reaching". Journal of Neurophysiology 98, n.º 6 (diciembre de 2007): 3600–3613. http://dx.doi.org/10.1152/jn.00121.2007.

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We examined control of the hand's trajectory (direction and shape) and final equilibrium position in horizontal planar arm movements by quantifying transfer of learned visuomotor rotations between two tasks that required aiming the hand to the same spatial targets. In a trajectory-reversal task (“slicing”), the hand reversed direction within the target and returned to the origin. In a positioning task (“reaching”), subjects moved the hand to the target and held it there; cursor feedback was provided only after movement ended to isolate learning of final position from trajectory direction. We asked whether learning acquired in one task would transfer to the other. Transfer would suggest that the hand's entire trajectory, including its endpoint, was controlled using a common spatial plan. Instead we found minimal transfer, suggesting that the brain used different representations of target position to specify the hand's initial trajectory and its final stabilized position. We also observed asymmetrical practice effects on hand trajectory, including systematic curvature of reaches made after rotation training and hypermetria of untrained slice reversals after reach training. These are difficult to explain with a unified control model, but were replicated in computer simulations that specified the hand's initial trajectory and its final equilibrium position. Our results suggest that the brain uses different mechanisms to plan the hand's initial trajectory and final position in point-to-point movements, that it implements these control actions sequentially, and that trajectory planning does not account for specific impedance values to be implemented about the final stabilized posture.

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Li, Xue y Zhiyong Geng. "A novel trajectory planning-based adaptive control method for 3-D overhead cranes". International Journal of Systems Science 49, n.º 16 (25 de octubre de 2018): 3332–45. http://dx.doi.org/10.1080/00207721.2018.1537412.

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33

Zhou, Xiang, Rui-Zhi He, Hong-Bo Zhang, Guo-Jian Tang y Wei-Min Bao. "Sequential convex programming method using adaptive mesh refinement for entry trajectory planning problem". Aerospace Science and Technology 109 (febrero de 2021): 106374. http://dx.doi.org/10.1016/j.ast.2020.106374.

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34

Zhang, Lunhui, Yong Wang, Xiaoyong Zhao, Ping Zhao y Liangguo He. "Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm". Journal of Mechanical Science and Technology 35, n.º 7 (29 de junio de 2021): 3171–81. http://dx.doi.org/10.1007/s12206-021-0638-5.

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35

Vu, N. T. T., N. P. Tran y N. H. Nguyen. "Recurrent Neural Network-based Path Planning for an Excavator Arm under Varying Environment". Engineering, Technology & Applied Science Research 11, n.º 3 (15 de junio de 2021): 7088–93. http://dx.doi.org/10.48084/etasr.4125.

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This paper proposes an algorithm to generate the reference trajectory based on recurrent neural networks for an excavator arm working in a dynamic environment. Firstly, the dynamic of the plant which includes the tracking controller, the arm, and the pile is appropriated by a recurrent neural network. Next, the recurrent neural network combined with a Model Reference Adaptive Controller (MRAC) is used to calculate the reference trajectory for the system. In this paper, the generated trajectory is changed depending on the variation of the pile to maximize the dug weight. This algorithm is simple but effective because it only needs information about the weight at each duty cycle of the excavator. The efficiency of the overall system is verified through simulations. The results show that the proposed scheme gives a good performance, i.e. the dug weight always remains at the desired value (nominal load) as the pile changes its shape during working time.

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36

Yu, Minghui, Xue Gong, Guowei Fan y Yu Zhang. "Trajectory Planning and Tracking for Carrier Aircraft-Tractor System Based on Autonomous and Cooperative Movement". Mathematical Problems in Engineering 2020 (13 de junio de 2020): 1–24. http://dx.doi.org/10.1155/2020/6531984.

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The solution of how to plan out the cooperative moving trajectory autonomously and control the motion of carrier-based aircraft timely and accurately is the key to helping improve the overall deck operation efficiency. The main problem discussed in this article is coordinated trajectory planning strategy for multicarrier aircraft and cooperative control between tractor and carrier aircraft. First, the kinematic model and three-degree-of-freedom dynamics model of the towbarless traction system are established. Then, a coevolution mechanism for aircraft systems is proposed to ensure coordinated trajectory planning among multiple aircraft and a trajectory adapted to the tractor-aircraft system is generated based on the hybrid RRT∗ algorithm. Next, a double-layer closed-loop controller is designed for the trajectory tracking of the tractor-aircraft system on the deck under the constraints of incomplete constraints and various physical conditions. It includes an outer model predictive controller which effectively controls the cooperative motion between the carrier aircraft and tractor and an inner torque control strategy based on adaptive fuzzy PID control which strictly ensures the stability of the system. Simulation results demonstrate that the controller is more rapid, more accurate, and more robust in tracking line trajectory with initial deviation, sine curve with large curvature, and complex trajectories on decks compared with backstepping control and LQR algorithm.

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37

Hidalgo, Carlos, Ray Lattarulo, Carlos Flores y Joshué Pérez Rastelli. "Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies". Sensors 21, n.º 8 (8 de abril de 2021): 2626. http://dx.doi.org/10.3390/s21082626.

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Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

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38

Kumagai, Iori, Mitsuharu Morisawa, Shin’ichiro Nakaoka y Fumio Kanehiro. "On-Site Locomotion Planning for a Humanoid Robot with Stable Whole-Body Collision Avoidance Motion Guided by Footsteps and Centroidal Trajectory". International Journal of Humanoid Robotics 17, n.º 01 (31 de diciembre de 2019): 1950035. http://dx.doi.org/10.1142/s021984361950035x.

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In this paper, we propose a locomotion planning framework for a humanoid robot with stable whole-body collision avoidance motion, which enables the robot to traverse an unknown narrow space on the spot based on environmental measurements. The key idea of the proposed method is to reduce a large computational cost for the whole-body locomotion planning by utilizing global footstep planning results and its centroidal trajectory as a guide. In the global footstep planning phase, we modify the bounding box of the robot approximating the centroidal sway amplitude of the candidate footsteps. This enables the planner to obtain appropriate footsteps and transition time for next whole-body motion planning. Then, we execute sequential whole-body motion planning by prioritized inverse kinematics considering collision avoidance and maintaining its ZMP trajectory, which enables the robot to plan stable motion for each step in 223[Formula: see text]ms at worst. We evaluated the proposed framework by a humanoid robot HRP-5P in the dynamic simulation and the real world. The major contribution of our paper is solving the problem of increasing computational cost for whole-body motion planning and enabling a humanoid robot to execute adaptive on-site locomotion planning in an unknown narrow space.

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39

Ferreira, Rafael Pereira y Américo Scotti. "The Concept of a Novel Path Planning Strategy for Wire + Arc Additive Manufacturing of Bulky Parts: Pixel". Metals 11, n.º 3 (17 de marzo de 2021): 498. http://dx.doi.org/10.3390/met11030498.

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An innovative trajectory strategy was proposed and accessed for wire arc additive manufacturing (WAAM), applicable to different and more complex geometries, rather than being a single solution. This strategy, named Pixel, can be defined as a complex multitask procedure to carry out optimized path planning, whose operation is made through computational algorithms (heuristics), with accessible computational resources and tolerable computational time. The model layers are fractioned in squared grids, and a set of dots is systematically generated and distributed inside the sliced outlines, resembling pixels on a screen, over which the trajectory is planned. The Pixel strategy was based on creating trajectories from the technique travelling salesman problem (TSP). Unlike existing algorithms, the Pixel strategy uses an adapted greedy randomized adaptive search procedure (GRASP) metaheuristic, aided by four concurrent trajectory planning heuristics, developed by the authors. Interactions provide successive trajectories from randomized initial solutions (global search) and subsequent iterative improvements (local search). After all recurrent loops, a trajectory is defined and written in machine code. Computational evaluation was implemented to demonstrate the effect of each of the heuristics on the final trajectory. An experimental evaluation was eventually carried out using two different not easily printable shapes to demonstrate the practical feasibility of the proposed strategy.

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40

Kang, Keeryun y J. V. R. Prasad. "Development and Flight Test Evaluations of an Autonomous Obstacle Avoidance System for a Rotary-Wing UAV". Unmanned Systems 01, n.º 01 (20 de junio de 2013): 3–19. http://dx.doi.org/10.1142/s2301385013500015.

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This paper presents the development and flight-testing of an obstacle avoidance system that can provide a rotary-wing unmanned aerial vehicle (UAV) the autonomous obstacle field navigation capability in uncertain environment. The system is composed of a sensor, an obstacle map generation algorithm from sensor measurements, an online path planning algorithm, and an adaptive vehicle controller. The novel approach of path planning presented in the paper is the integration of a newly developed receding horizon (RH) trajectory optimization scheme with a global path searching algorithm. The developed RH trajectory optimization scheme solves the local nonlinear trajectory optimization problem using approximated vehicle dynamics, maneuverability constraints, and terrain constraints within the finite range of the sensor. The global path searching by dynamic programming algorithm finds the shortest path to the destination to provide the initial guess to the RH trajectory optimization. The spline-based direct solver, Nonlinear Trajectory Generation (NTG), solves the RH trajectory optimization in real time and updates the solution continuously. The developed system is implemented within the Georgia Tech UAV Simulation Tool (GUST) and on the onboard computer of the Georgia Tech UAV test bed. Simulations and flight tests carried out for the benchmark scenarios and with sensor-in-the-loop flight tests demonstrated the viability of the developed system for autonomous obstacle field navigation capability of a UAV.

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41

Aguilar-Ibanez, Carlos y Miguel S. Suarez-Castanon. "A Trajectory Planning Based Controller to Regulate an Uncertain 3D Overhead Crane System". International Journal of Applied Mathematics and Computer Science 29, n.º 4 (1 de diciembre de 2019): 693–702. http://dx.doi.org/10.2478/amcs-2019-0051.

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Abstract We introduce a control strategy to solve the regulation control problem, from the perspective of trajectory planning, for an uncertain 3D overhead crane. The proposed solution was developed based on an adaptive control approach that takes advantage of the passivity properties found in this kind of systems. We use a trajectory planning approach to preserve the accelerations and velocities inside of realistic ranges, to maintaining the payload movements as close as possible to the origin. To this end, we carefully chose a suitable S-curve based on the Bezier spline, which allows us to efficiently handle the load translation problem, considerably reducing the load oscillations. To perform the convergence analysis, we applied the traditional Lyapunov theory, together with Barbalat’s lemma. We assess the effectiveness of our control strategy with convincing numerical simulations.

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42

Asif, Umar y Javaid Iqbal. "Motion Planning Using an Impact-Based Hybrid Control for Trajectory Generation in Adaptive Walking". International Journal of Advanced Robotic Systems 8, n.º 4 (enero de 2011): 53. http://dx.doi.org/10.5772/45701.

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43

Yuan, Chenxi y Hubo Cai. "Spatial reasoning mechanism to enable automated adaptive trajectory planning in ground penetrating radar survey". Automation in Construction 114 (junio de 2020): 103157. http://dx.doi.org/10.1016/j.autcon.2020.103157.

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44

Shah, Brual C., Petr Švec, Ivan R. Bertaska, Armando J. Sinisterra, Wilhelm Klinger, Karl von Ellenrieder, Manhar Dhanak y Satyandra K. Gupta. "Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic". Autonomous Robots 40, n.º 7 (21 de diciembre de 2015): 1139–63. http://dx.doi.org/10.1007/s10514-015-9529-x.

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45

De Oliveira Lima, Jean Phelipe, Raimundo Correa de Oliveira y Cleinaldo de Almeida Costa. "Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning". International Journal for Innovation Education and Research 8, n.º 12 (1 de diciembre de 2020): 436–54. http://dx.doi.org/10.31686/ijier.vol8.iss12.2837.

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Autonomous vehicle path planning aims to allow safe and rapid movement in an environment without human interference. Recently, Reinforcement Learning methods have been used to solve this problem and have achieved satisfactory results. This work presents the use of Deep Reinforcement Learning for the task of path planning for autonomous vehicles through trajectory simulation, to define routes that offer greater safety (without collisions) and less distance for the displacement between two points. A method for creating simulation environments was developed to analyze the performance of the proposed models in different difficult degrees of circumstances. The decision-making strategy implemented was based on the use of Artificial Neural Networks of the Multilayer Perceptron type with parameters and hyperparameters determined from a grid search. The models were evaluated for their reward charts resulting from their learning process. Such evaluation occurred in two phases: isolated evaluation, in which the models were inserted into the environment without prior knowledge; and incremental evaluation, in which models were inserted in unknown environments with previous intelligence accumulated in other conditions. The results obtained are competitive with state-of-the-art works and highlight the adaptive characteristic of the models presented, which, when inserted with prior knowledge in environments, can reduce the convergence time by up to 89.47% when compared to related works.

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46

Nakrani, Naitik y Maulin M. Joshi. "An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking". IAES International Journal of Artificial Intelligence (IJ-AI) 10, n.º 3 (1 de septiembre de 2021): 687. http://dx.doi.org/10.11591/ijai.v10.i3.pp687-697.

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In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

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47

Zhao, Yinghao, Li Yan, Yu Chen, Jicheng Dai y Yuxuan Liu. "Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight". Remote Sensing 13, n.º 5 (4 de marzo de 2021): 972. http://dx.doi.org/10.3390/rs13050972.

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Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.

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48

Meghdari, Ali, Seyyed Mohammad H. Lavasani, Mohsen Norouzi y Mir Saman Rahimi Mousavi. "Minimum control effort trajectory planning and tracking of the CEDRA brachiation robot". Robotica 31, n.º 7 (14 de mayo de 2013): 1119–29. http://dx.doi.org/10.1017/s0263574713000362.

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SUMMARYThe control of a brachiation robot has been the primary objective of this study. A brachiating robot is a type of a mobile arm that is capable of moving from branch to branch similar to a long-armed ape. In this paper, to minimize the actuator work, Pontryagin's minimum principle was used to obtain the optimal trajectories for two different problems. The first problem considers “brachiation between fixed branches with different distance and height,” whereas the second problem deals with the “brachiating and catching of a moving target branch”. Theoretical results show that the control effort in the proposed method is reduced by 25% in comparison with the “target dynamics” method which was proposed by Nakanishi et al. (1998)16 for the same type of robot. As a result, the obtained optimal trajectory also minimizes the brachiation time. Two kinds of controllers, namely the proportional-derivative (PD) and the adaptive robust (AR), were investigated for tracking the proposed trajectories. Then, the previous method on a set-point controller for acrobat robots is improved to represent a new AR controller which allows the system to track the desired trajectory. This new controller has the capability to be used in systems which have uncertainties in the kinematic and dynamic parameters. Finally, theoretical results are presented and validated with experimental observations with a PD controller due to the no chattering phenomenon and small computational efforts.

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49

Wang, Pengyu, Jinke Li, Yuanbin Yu, Xiaoyong Xiong, Shijie Zhao y Wangsheng Shen. "Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction". Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, n.º 8 (19 de marzo de 2020): 2239–59. http://dx.doi.org/10.1177/0954407020904464.

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In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

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50

Ortiz, Alvaro, Sergio Garcia-Nieto y Raul Simarro. "Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking". Electronics 10, n.º 3 (1 de febrero de 2021): 331. http://dx.doi.org/10.3390/electronics10030331.

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Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness.

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