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Llopis-Albert, Carlos, Francisco Rubio, and Francisco Valero. "Optimization approaches for robot trajectory planning." Multidisciplinary Journal for Education, Social and Technological Sciences 5, no. 1 (March 29, 2018): 1. http://dx.doi.org/10.4995/muse.2018.9867.
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<p class="Textoindependiente21">The development of optimal trajectory planning algorithms for autonomous robots is a key issue in order to efficiently perform the robot tasks. This problem is hampered by the complex environment regarding the kinematics and dynamics of robots with several arms and/or degrees of freedom (dof), the design of collision-free trajectories and the physical limitations of the robots. This paper presents a review about the existing robot motion planning techniques and discusses their pros and cons regarding completeness, optimality, efficiency, accuracy, smoothness, stability, safety and scalability.</p>
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Bae, Hyansu, Gidong Kim, Jonguk Kim, Dianwei Qian, and Sukgyu Lee. "Multi-Robot Path Planning Method Using Reinforcement Learning." Applied Sciences 9, no. 15 (July 29, 2019): 3057. http://dx.doi.org/10.3390/app9153057.
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This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.
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Chen, Hai Long, Xiao Wu, Jun Du, and Jin Ping Tang. "Biped Walking Robot Gait Planning Research." Advanced Materials Research 706-708 (June 2013): 674–77. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.674.
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This paper uses biped walking robot as the research object, and designs robots original system, based on the requirements of Biped Walking Robot Competition of China. According to the biped walking robots characteristics of multi-joints, many degrees of freedom, multivariable, strong coupling and nonlinearity [, we can build system model using the Denavi - Hartenberg coordinate, describe the system model by the homogeneous coordinate transformation theory, and then plan on system gait based on ZMP stability . Finally, we can solve for the joint trajectory of the system by using computer-aided software.
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Karpas, Erez, and Daniele Magazzeni. "Automated Planning for Robotics." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 417–39. http://dx.doi.org/10.1146/annurev-control-082619-100135.
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Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
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Noborio, Hiroshi, and Takashi Tsubouchi. "Special Issue on Robot Motion Planning." Journal of Robotics and Mechatronics 8, no. 1 (February 20, 1996): 1. http://dx.doi.org/10.20965/jrm.1996.p0001.
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This special issue is devoted to robot motion planning. The main scope of this issue covers research work on mobile robotics. Motion planning is necessary when the robot determines its own actions. For the last decade, the paradigm of motion planning in mobile robotics has shifted from off-line motion planning to on-line motion planning and from planning in a static environment to planning in a time-varying environment. Recent progress of computational power has enabled this paradigm shift, since on-line motion planning and planning in time-varying environments require repeated computation based on sensory information which is always renewed. The guest editors organized this special issue in order to highlight those two new paradigms. We present two survey papers: One is a survey of on-line motion planning for a sensor-based navigation of a mobile robot, and the other is a survey of motion planning for mobile robots in a time-varying environment. Along with the survey papers, distinguished technical papers are provided in this special issue. Concerning path planning, a paper describing a case study on motion planning with teaching is included (Ogata et al). Motion planning based on Fuzzy logic is one approach, and three papers from Maeda, Ishikawa et al. and Nagata et al. also belong to this category. To offer a case study on reactive motion decision making, one paper by Ando et al. is included. A recently emerging subject is related to motion planning for cooperation of multiple mobile robots or a single robot among multiple moving obstacles. Three papers from Yoshioka et al., Ota et al., and Fujimura discuss problems on motion planning for cooperation of multiple mobile robots. One paper from Tsubouchi et al. discussed the motion planning of a single robot among multiple moving obstacles. Motion planning to select an appropriate corner cube as a landmark is addressed in the paper from Hashimoto et al. The guest editors hope that this special issue will provide the readers with a lock at some current issues and new perspectives on robot motion planning.
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Panchu, K. Padmanabhan, M. Rajmohan, M. R. Sumalatha, and R. Baskaran. "Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 627–36. http://dx.doi.org/10.1166/jctn.2018.7137.
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This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.
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Sun, Zhang, and Chen. "RTPO: A Domain Knowledge Base for Robot Task Planning." Electronics 8, no. 10 (October 1, 2019): 1105. http://dx.doi.org/10.3390/electronics8101105.
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Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.
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Shin, Youshik, and Zeungnam Bien. "Collision–Free Trajectory Planning for Two Robot Arms." Robotica 7, no. 3 (July 1989): 205–12. http://dx.doi.org/10.1017/s0263574700006068.
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SUMMARYAn approach for collision–free trajectory planning along designated paths of two robots in a common workspace is presented. Specifically, in order to describe potential collision between the links of two robots along the designated paths, explicit forms of virtual obstacle are adopted, according to which links of one robot are made to grow while the other robot is forced to shrink as a point on the path. Then, a notion of virtual coordination space is introduced to visualize all the collision–free coordinations of two trajectories. Assuming that a collision–free coordination curve between the two robots is given via a virtual coordination space, the minimum time collision–free trajectory pair for the two robots is sought considering dynamic constraints of torque and velocity bounds of actuators of the two robots.
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Chiu, Min Chie, Long Jyi Yeh, Tian Syung Lan, and Shao Chun Yen. "Positioning and Path Planning for a Swarm Robotic Cleaner." Advanced Materials Research 740 (August 2013): 112–19. http://dx.doi.org/10.4028/www.scientific.net/amr.740.112.
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The main purpose of this paper is to create an efficient ground-sweeping robot equipped with map-establishing and path-planning functions. Two ground-sweeping robots are connected with a master pc via a Blue-tooth protocol. The position of the ground-sweeping robot will be sent back to the master pc allowing the master pc to control the robots during the ground-sweeping process. An environmental map of the sweeping area will be established by emitting an ultrasonic wave from a rotating ultrasonic sensor within the robot. The geometry data will be sent back to the master pc via the Bluetooth module. The map of sweeping area will be made by the master pc using a wall-searching method. A single-chip Microcontroller PIC18F4520 is used as a control core to control the motor speed via the PWM in the robot. The clockwise and counter clockwise rotation of the motor will then be manipulated by a TA7279 IC. The robot is equipped with two ultrasonic modules used to detect the distance between the robot and the obstacle. This information will be sent back to the master pc via the Blue-tooth module. Consequently, results reveal that a prototype of the swarm robot system using two ground-sweeping robots and a master pc has positioning and mapping abilities.
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Alterovitz, Ron, Sven Koenig, and Maxim Likhachev. "Robot Planning in the Real World: Research Challenges and Opportunities." AI Magazine 37, no. 2 (July 4, 2016): 76–84. http://dx.doi.org/10.1609/aimag.v37i2.2651.
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Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. These robots are often manually programmed, teleoperated, or programmed to follow simple rules. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.
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Gao, Mingyu, Da Chen, Yuxiang Yang, and Zhiwei He. "A fixed-distance planning algorithm for 6-DOF manipulators." Industrial Robot: An International Journal 42, no. 6 (October 19, 2015): 586–99. http://dx.doi.org/10.1108/ir-04-2015-0077.
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Purpose – The purpose of this paper is to propose a new trajectory planning algorithm for industrial robots, which can let the robots move through a desired spatial trajectory, avoid colliding with other objects and achieve accurate movements. Trajectory planning algorithms are the soul of motion control of industrial robots. A predefined space trajectory can let the robot move through the desired spatial coordinates, avoid colliding with other objects and achieve accurate movements. Design/methodology/approach – The mathematical expressions of the proposed algorithm are deduced. The speed control, position control and orientation control strategies are realized and verified with simulations, and then implemented on a six degrees of freedom (6-DOF) industrial robot platform. Findings – A fixed-distance trajectory planning algorithm based on Cartesian coordinates was presented. The linear trajectory, circular trajectory, helical trajectory and parabolic trajectory in Cartesian coordinates were implemented on the 6-DOF industrial robot. Originality/value – A simple and efficient algorithm is proposed. Enrich the kind of trajectory which the industrial robot can realize. In addition, the industrial robot can move more concisely, smoothly and precisely.
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Zhou, Guanghong. "Human-Machine Cooperation and Path Planning for Complex Road Conditions." Scientific Programming 2021 (July 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/7262281.
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With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable.
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Yang, Liang, Juntong Qi, Dalei Song, Jizhong Xiao, Jianda Han, and Yong Xia. "Survey of Robot 3D Path Planning Algorithms." Journal of Control Science and Engineering 2016 (2016): 1–22. http://dx.doi.org/10.1155/2016/7426913.
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Robot 3D (three-dimension) path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints (including geometric, physical, and temporal constraints). The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.
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ElMaraghy, H. A., and J. M. Rondeau. "Automated planning and programming environments for robots." Robotica 10, no. 1 (January 1992): 75–82. http://dx.doi.org/10.1017/s0263574700007098.
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SummaryTraditionally, most industrial robots are programmed by teaching. Automatic planning of robotic tasks has many potential benefits for flexible automation. It allows the user to describe a task to the robot programming system in a formal and natural manner, and reduces the time required to generate and update robot programs. Two main levels of abstraction in describing robot tasks can be identified. Robot-level programming is based on robot movements and actions, as detailed by the programmer. Object-level or task-level programming allows the user to describe assembly tasks in terms of operations performed on objects being manipulated instead of specifying the individual motions of the robot end-effector. However, commercially available robot-level programming languages still fall short of the robot user's need to programme complex tasks and consequently are not widely used in industry. There is an increasing need for integrating sensors feedback into the robot system to provide better perception and for improving the capacity of the robot to reason and make decisions intelligently in real-time. Task-level programming represents the highest level of abstraction and is the most attractive, as it uses reasoning capabilities provided by Artificial Intelligence. To date, no system of this class has been completely implemented in industry. This paper reviews the progress made in robot programming and task planning systems in the last twenty years, and discusses the current research trends.
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Solovey, Kiril, and Dan Halperin. "On the hardness of unlabeled multi-robot motion planning." International Journal of Robotics Research 35, no. 14 (November 1, 2016): 1750–59. http://dx.doi.org/10.1177/0278364916672311.
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In unlabeled multi-robot motion planning, several interchangeable robots operate in a common workspace. The goal is to move the robots to a set of target positions such that each position will be occupied by some robot. In this paper, we study this problem for the specific case of unit-square robots moving amidst polygonal obstacles and show that it is PSPACE-hard. We also consider three additional variants of this problem and show that they are all PSPACE-hard as well. To the best of our knowledge, this is the first hardness proof for the unlabeled case. Furthermore, our proofs can be used to show that the labeled variant (where each robot is assigned a specific target position), again, for unit-square robots, is PSPACE-hard as well, which sets another precedent, as previous hardness results require the robots to be of different shapes (or at least in different orientations). Lastly, we settle an open problem regarding the complexity of the well-known Rush-Hour puzzle for unit-square cars in environments with polygonal obstacles.
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Zhang, Hong Min. "Path Planning Methods of Mobile Robot Based on Soft Computing Technique." Advanced Materials Research 216 (March 2011): 677–80. http://dx.doi.org/10.4028/www.scientific.net/amr.216.677.
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Path planning is one of the most important and challenging problems of mobile robot. It is one of the keys that will make the mobile robots fully autonomous. In this paper, we summarized the application of soft computing approaches in path planning for mobile robot. Finally the future works of path planning for mobile robots are prospected.
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Matignon, Laetitia, Laurent Jeanpierre, and Abdel-Illah Mouaddib. "DECENTRALIZED MULTI-ROBOT PLANNING TO EXPLORE AND PERCEIVE." Acta Polytechnica 55, no. 3 (June 30, 2015): 169–76. http://dx.doi.org/10.14311/ap.2015.55.0169.
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In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition.
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Gong, Li, Yong Zhang, and Jin Cheng. "Coordinated Path Planning Based on RRT Algorithm for Robot." Applied Mechanics and Materials 494-495 (February 2014): 1003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1003.
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In this paper, two robots in different positions with two different paths are planned based on RRT algorithm. And two robots search the ball according to the planned paths coordinated with each other under the circumstance of existing static obstacles. Simulation result showed that the correctness of the planned path. RRT algorithm is suitable for path planning of high dimensional robot under complex environment, which avoids complicated modeling of robot and obstacles in C space. And RRT algorithm uses tree structure, which tracks between adjacent node accord with requirements of robots kinematics, dynamics and avoiding collision. Therefore, it is suitable for practical application of robots.
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Lixia, Fang, Tong Wang, Yang Shen, Pengjiang Wang, and Miao Wu. "Parallel collaborative planning for the coupled system of underground heavy-load robot." Advances in Mechanical Engineering 13, no. 4 (April 2021): 168781402110059. http://dx.doi.org/10.1177/16878140211005969.
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At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.
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Nikou, Alexandros, Shahab Heshmati-alamdari, and Dimos V. Dimarogonas. "Scalable time-constrained planning of multi-robot systems." Autonomous Robots 44, no. 8 (July 31, 2020): 1451–67. http://dx.doi.org/10.1007/s10514-020-09937-6.
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Abstract This paper presents a scalable procedure for time-constrained planning of a class of uncertain nonlinear multi-robot systems. In particular, we consider N robotic agents operating in a workspace which contains regions of interest (RoI), in which atomic propositions for each robot are assigned. The main goal is to design decentralized and robust control laws so that each robot meets an individual high-level specification given as a metric interval temporal logic (MITL), while using only local information based on a limited sensing radius. Furthermore, the robots need to fulfill certain desired transient constraints such as collision avoidance between them. The controllers, which guarantee the transition between regions, consist of two terms: a nominal control input, which is computed online and is the solution of a decentralized finite-horizon optimal control problem (DFHOCP); and an additive state feedback law which is computed offline and guarantees that the real trajectories of the system will belong to a hyper-tube centered along the nominal trajectory. The controllers serve as actions for the individual weighted transition system (WTS) of each robot, and the time duration required for the transition between regions is modeled by a weight. The DFHOCP is solved at every sampling time by each robot and then necessary information is exchanged between neighboring robots. The proposed approach is scalable since it does not require a product computation among the WTS of the robots. The proposed framework is experimentally tested and the results show that the proposed framework is promising for solving real-life robotic as well as industrial applications.
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McQueen, Keimargeo, Sara Darensbourg, Carl Moore, Tarik Dickens, and Clement Allen. "Efficient Path Planning of Secondary Additive Manufacturing Operations." MATEC Web of Conferences 249 (2018): 03011. http://dx.doi.org/10.1051/matecconf/201824903011.
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We have designed a path planner for an additive manufacturing (AM) prototype that consists of two robotic arms which collaborate on a single part. Theoretically, with two nozzle equipped arms, a part can be 3D printed twice as fast. Moreover, equipping the second robot with a machining tool enables the completion of secondary operations like hole reaming or surface milling before the printing is finished. With two arms in the part space care must be taken to ensure that the arms collaborate intelligently; in particular, tasks must be planned so that the robots do not collide. This paper discusses the development of a robot path planner to efficiently print segments with two arms, while maintaining a safe distance between them. A solution to the travelling salesman problem, an optimal path planning problem, was used to successfully determine the robots path plans while a simple nozzle-to-nozzle distance calculation was added to represent avoiding robot-to-robot collisions. As a result, in simulation, the average part completion time was reduced by 45% over the single nozzle case. Importantly, the algorithm can theoretically be run on n-robots, so time reduction possibilities are large.
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Zhou, Hao. "Research on Trajectory Planning of Six Degrees of Freedom Robot." ITM Web of Conferences 25 (2019): 01010. http://dx.doi.org/10.1051/itmconf/20192501010.
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With the continuous development of industrial automation, the demand for industrial robots in the manufacturing field is gradually increasing. In order to meet the needs of different occasions and functions, the planning of the trajectory of the robot becomes the research direction of the six-degree-of-freedom robot. The research object of this paper is a six-degree-of-freedom industrial robot. According to engineering needs, a structure of a handling robot is designed. The kinematics of the robot and its trajectory planning are studied, and the simulation analysis is made.
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Sharir, Micha. "Robot motion planning." Communications on Pure and Applied Mathematics 48, no. 9 (1995): 1173–86. http://dx.doi.org/10.1002/cpa.3160480910.
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Spur, G., I. Furgac, A. Deutschländer, J. Browne, and P. O'Gorman. "Robot planning system." Robotics and Computer-Integrated Manufacturing 2, no. 2 (January 1985): 115–23. http://dx.doi.org/10.1016/0736-5845(85)90067-5.
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Ledziński, Damian, Tomasz Marciniak, Mirosław Maszewski, and Dariusz Boroński. "Robot Actions Planning Algorithms in Multi-Agent System." Solid State Phenomena 223 (November 2014): 221–30. http://dx.doi.org/10.4028/www.scientific.net/ssp.223.221.
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In this paper, the basic information about multi-agent systems is given. The authors propose robot control algorithms for managing virtual autonomous warehouses, where the task performed by the robots is transportation between specific locations in the warehouse and a number of distribution points. Algorithms control the work of a single robot, including the cooperation with other robots in the environment as well as collisions avoidance. Different routing algorithms are evaluated through simulations focusing on service time and waiting time of executing tasks. The impact of the proposed algorithms on energy consumption was also checked, since this is important for the working time between battery charges.
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Shao, Jie, Hai Xia Lin, and Bin Song. "A Hierarchical Conflict Resolution Method for Multi-Robot Path Planning." Applied Mechanics and Materials 380-384 (August 2013): 1482–87. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1482.
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Multi-robot path planning using shared resources, easily conflict, prioritisation is the shared resource conflicts to resolve an important technology. This paper presents a learning classifier based on dynamic allocation of priority methods to improve the performance of the robot team. Individual robots learn to optimize their behaviors first, and then a high-level planner robot is introduced and trained to resolve conflicts by assigning priority. The novel approach is designed for Partially Observable Markov Decision Process environments. Simulation results show that the method used to solve the conflict in multi-robot path planning is effective and improve the capacity of multi-robot path planning.
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Pambudi, Wahyu S., Enggar Alfianto, Andy Rachman, and Dian Puspita Hapsari. "Simulation design of trajectory planning robot manipulator." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 196–205. http://dx.doi.org/10.11591/eei.v8i1.1179.
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Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
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BOISSONNAT, JEAN-DANIEL, OLIVIER DEVILLERS, LEONBATTISTA DONATI, and FRANCO P. PREPARATA. "MOTION PLANNING OF LEGGED ROBOTS: THE SPIDER ROBOT PROBLEM." International Journal of Computational Geometry & Applications 05, no. 01n02 (March 1995): 3–20. http://dx.doi.org/10.1142/s0218195995000027.
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We consider the problem of planning motions of a simple legged robot called the spider robot. The robot is modelled as a point where all its legs are attached, and the footholds where the robot can securely place its feet consist of a set of n points in the plane. We show that the space F of admissible and stable placements of such robots has size Θ(n2) and can be constructed in O(n2 log n) time and O(n2) space. Once F has been constructed, we can efficiently solve several problems related to motion planning.
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Kootbally, Zeid. "Industrial robot capability models for agile manufacturing." Industrial Robot: An International Journal 43, no. 5 (August 15, 2016): 481–94. http://dx.doi.org/10.1108/ir-02-2016-0071.
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Purpose This paper aims to represent a capability model for industrial robot as they pertain to assembly tasks. Design/methodology/approach The architecture of a real kit building application is provided to demonstrate how robot capabilities can be used to fully automate the planning of assembly tasks. Discussion on the planning infrastructure is done with the Planning Domain Definition Language (PDDL) for heterogeneous multi robot systems. Findings The paper describes PDDL domain and problem files that are used by a planner to generate a plan for kitting. Discussion on the plan shows that the best robot is selected to carry out assembly actions. Originality/value The author presents a robot capability model that is intended to be used for helping manufacturers to characterize the different capabilities their robots contribute to help the end user to select the appropriate robots for the appropriate tasks, selecting backup robots during robot’s failures to limit the deterioration of the system’s productivity and the products’ quality and limiting robots’ failures and increasing productivity by providing a tool to manufacturers that outputs a process plan that assigns the best robot to each task needed to accomplish the assembly.
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Trujillo, Juan-Carlos, Rodrigo Munguia, and Antoni Grau. "Aerial Cooperative SLAM for Ground Mobile Robot Path Planning." Engineering Proceedings 6, no. 1 (May 20, 2021): 65. http://dx.doi.org/10.3390/i3s2021dresden-10164.
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The trajectory planning for ground mobile robots operating in unknown environments can be a difficult task. In many cases, the sensors used for detecting obstacles only provide information about the immediate surroundings, making it difficult to generate an efficient long-term path. For instance, a robot can easily choose to move along a free path that, eventually, will have a dead end. This research is intended to develop a cooperative scheme of visual-based aerial simultaneous localization and mapping (SLAM) that will be used for generating a safe long-term trajectory for a ground mobile robot. The general idea is to take advantage of the high-altitude point of view of aerial robots to obtain spatial information of a wide area of the surroundings of the robot. In this case, it could be seen as having a zenithal picture of the labyrinth to solve the robot’s path. More specifically, the system will generate a wide area spatial map of the ground robot’s obstacles from the images taken by a team of aerial robots equipped with onboard cameras, by means of a cooperative visual-based SLAM method. At the same time, the map will be used to generate a safe path for the ground mobile robot. While the ground robot moves, its onboard sensors will be used to refinine the map and, thus, to avoid obstacles that were not detected from the aerial images.
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Vasu, V., and K. Jyothi Kumar. "Optimal Path Planning of an Autonomous Mobile Robot Using Genetic Algorithm." Advanced Materials Research 488-489 (March 2012): 1747–51. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1747.
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An autonomous Mobile Robot (AMR) is a machine able to extract information from its environment and move in a meaningful and purposeful manner. Robot Navigation and Obstacle avoidance are the most important problems in mobile robots. In the past, a number of soft computing algorithms have been designed by many researchers for robot navigation problems but very few are actually implementable because they haven’t considered robot size as parameter. This paper presents software simulation and hardware implementation of navigation of a mobile robot avoiding obstacles and selecting optimal path in a static environment using evolution based Genetic algorithms with robot size as a parameter in fitness function.
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FALLAHI, KIA, and HENRY LEUNG. "A COOPERATIVE MOBILE ROBOT TASK ASSIGNMENT AND COVERAGE PLANNING BASED ON CHAOS SYNCHRONIZATION." International Journal of Bifurcation and Chaos 20, no. 01 (January 2010): 161–76. http://dx.doi.org/10.1142/s021812741002548x.
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In this paper, we propose a cooperative task assignment and coverage planning for mobile robots based on chaos synchronization. The chaotic mobile robot implies that the robot controller that drives a chaotic motion is characterized by topological transitivity and sensitive dependence on initial conditions. Due to the topological transitivity, the chaotic mobile robot is guaranteed to scan a workspace completely and the robot requires neither a map of the workspace nor a global motion plan. Chen and Lorenz systems are used to generate chaotic motion in this work. Cooperative multirobot systems can operate faster with higher efficiency and better reliability than a single robot system. By synchronizing the chaotic robot controllers, effective cooperation can be achieved. The performance of the cooperative chaotic mobile robots can be attributed to the use of deterministic dynamical systems and extended Kalman filter for chaos synchronization. Computer simulations illustrate the effectiveness of the proposed approach.
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Zeng, Rui, Yuhui Wen, Wang Zhao, and Yong-Jin Liu. "View planning in robot active vision: A survey of systems, algorithms, and applications." Computational Visual Media 6, no. 3 (August 1, 2020): 225–45. http://dx.doi.org/10.1007/s41095-020-0179-3.
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Abstract Rapid development of artificial intelligence motivates researchers to expand the capabilities of intelligent and autonomous robots. In many robotic applications, robots are required to make planning decisions based on perceptual information to achieve diverse goals in an efficient and effective way. The planning problem has been investigated in active robot vision, in which a robot analyzes its environment and its own state in order to move sensors to obtain more useful information under certain constraints. View planning, which aims to find the best view sequence for a sensor, is one of the most challenging issues in active robot vision. The quality and efficiency of view planning are critical for many robot systems and are influenced by the nature of their tasks, hardware conditions, scanning states, and planning strategies. In this paper, we first summarize some basic concepts of active robot vision, and then review representative work on systems, algorithms and applications from four perspectives: object reconstruction, scene reconstruction, object recognition, and pose estimation. Finally, some potential directions are outlined for future work.
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Satake, Toshifumi, Akihiro Hayashi, and Hiroshi Suzuki. "The Use of Self-Organizing Cells in Robot Motion Planning." Journal of Robotics and Mechatronics 6, no. 6 (December 20, 1994): 479–84. http://dx.doi.org/10.20965/jrm.1994.p0479.
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Robot motion planning including obstacle avoidance is a necessary technique for realizing autonomous intelligent robots. In this study, a robot motion planning algorithm has been developed by applying the organic mechanisms such as artificial life (AL) and GAs. The proposed concept represents a robot as a combination of several groups composed of a set of cells defined as autonomous elements like a living thing. Robot motion is determined as the result of behavior that individual cells avoid obstacles, and they then organize themselves as forming a geometric shape of component parts of a robot according to the control mechanism given to each cell. This paper describes the preliminary concepts of the Obstacle Avoidance and Self Organization in the proposed methodology for generating collision-free motion of a robot and the details of the developed algorithm based on these concepts. To estimate the effectiveness of the proposed idea, simple case studies of the motion planning for mobile robot have been executed.
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Huang, Yiqing, Zhikun Li, Yan Jiang, and Lu Cheng. "Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy." Applied Sciences 9, no. 4 (February 16, 2019): 672. http://dx.doi.org/10.3390/app9040672.
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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.
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Hert, Susan, and Vladimir Lumelsky. "Planar Curve Routing for Tethered-Robot Motion Planning." International Journal of Computational Geometry & Applications 07, no. 03 (June 1997): 225–52. http://dx.doi.org/10.1142/s0218195997000156.
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The following problem appears in robotics. A number of small, circular robots live in a common planar workspace. Each is attached by a flexible cable of finite length to a point on the boundary of this workspace. Each robot has a target point in the workspace it must reach. When a robot has reached its target, its cable will have been dragged out into the workspace and possibly pushed and bent by other robots. The cables remain taut at all times and may not overlap, but may bend around other robots. When only the target points are specified for the robots, their motion can produce arbitrarily complex cable configurations. The more complex a cable configuration is, the more restrictive it is to the motion of the robots. To keep restrictions on the robots' motions at a minimum, it is necessary to specify, in addition to the target points, a configuration of the cables that is as simple as possible but allows all robots to reach their targets. The problem of finding the simplest cable configuration for a given set of target points is shown to reduce to the problem of finding a minimal set of nonintersecting routes in a Euclidean graph whose nodes are the robots' cable line. That no set of edges can intersect any other set of edges is an unusual characteristic of this graph problem. This consideration leads to interesting geometric analysis used to determine which relative placements of the graph edges represent overlapping cable lines. An algorithm is suggested that uses an exhaustive search method with pruning to find a set of nonintersecting routes in the graph that is minimal according to a chosen criterion. The algorithm has been implemented and tested; examples of its performance are given.
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Tabarah, E., B. Benhabib, and R. G. Fenton. "Motion Planning for Cooperative Robotic Systems Performing Contact Operations." Journal of Mechanical Design 116, no. 4 (December 1, 1994): 1177–80. http://dx.doi.org/10.1115/1.2919505.
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Two algorithms are developed in this paper for the optimal motion coordination of a pair of industrial robots engaged in contact operations such as part machining or deburring. In the first algorithm the two robots jointly grasp a tool, and move it in such a way that it performs a prescribed contact operation along the surface of a workpiece. In the second algorithm, one robot grasps the tool while a second robot grasps and maneuvers the workpiece; both robots move simultaneously relative to each other so that the tool maintains contact with the workpiece while tracking a prescribed trajectory along its surface at a constant speed.
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Wang, Zhaotian, Yezhuo Li, and Yan-An Yao. "Motion and path planning of a novel multi-mode mobile parallel robot based on chessboard-shaped grid division." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 390–400. http://dx.doi.org/10.1108/ir-01-2018-0001.
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Purpose The purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD). Design/methodology/approach Based on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground. Findings The assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis. Originality/value The work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.
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Trnka, Kamil, and Pavol Božek. "Optimal Motion Planning of Spot Welding Robot Applications." Applied Mechanics and Materials 248 (December 2012): 589–93. http://dx.doi.org/10.4028/www.scientific.net/amm.248.589.
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This paper discusses the problem of effective motion planning for industrial robots. The first part dealt with current method for off-line motion planning. In the second part the presented work is done by one of the simulation systems with automatic trajectory generation and off-line programming capability. A spot welding process is involved. The practical application of this step strongly depends on the method for robot path optimization with high accuracy, thus transform the path into a time and energy optimal robot program for the real world, which is discussed in the third step.
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Zhang, Dai Yuan, and Peng Fu. "Robot Path Planning by Generalized Ant Colony Algorithm." Applied Mechanics and Materials 494-495 (February 2014): 1229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1229.
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For the problem that the searching speed of traditional ant colony algorithm in robot path planning problem is slow, this paper will solve this problem with generalized ant colony algorithm. Generalized ant colony algorithm extends the definition of ant colony algorithm and does more general research for ant colony algorithm. Functional update strategy replaces the parametric algorithm update strategy; it accelerates the convergence speed of ant colony algorithm. Applying the generalized ant colony algorithm to robot path planning problem can improve the searching speed of robots and reduce the cost of convergence time.
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Jiang, Kai, and Chun Gui Li. "Path Planning Based on Fuzzy Logic Algorithm for Robots in Hierarchical Control." Applied Mechanics and Materials 644-650 (September 2014): 701–4. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.701.
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To find the optimal path of mobile robots,a novel robot path planning strategy based on hierarchical control fuzzy algorithm has been proposed.The path planning strategy which developed to overcome the collision and avoidance problem in path planning of robot is inspired by fuzzy control concept,in order to achieving a target that making robots to follow a non-collision rapid and accurate path in uncertain environment.Simulation results showed that the strategy using fuzzy algorithm could meet the feasibility and validity demand.
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Das, Subir Kumar, Ajoy Kumar Dutta, and Subir Kumar Debnath. "OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1646. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1646-1656.
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<span>For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots’ path planning OperativeCriticalPointBug (OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle’s dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.</span>
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Zhen, Zhang, Cao Qixin, Charles Lo, and Zhang Lei. "A CORBA-based simulation and control framework for mobile robots." Robotica 27, no. 3 (May 2009): 459–68. http://dx.doi.org/10.1017/s026357470800489x.
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SUMMARYThis paper presents a distributed multiple mobile robots framework which allows programming and control of virtual and real mobile robots. The system provides the map building, path planning, robot task planning, simulation, and actual robot control functions in an indoor environment. Users can program the virtual robots in a customized simulation environment and check the performance of execution, i.e., if the simulation result is satisfying, users can download the code to a real robot. The paper focuses on the distributed architecture and key technologies of virtual robots simulation and control of real robots. A method for construction and transfer of a key index value (which stores the robot configuration) is proposed. Using this method, only the robot key configuration index is needed to build the robot in the virtual environment. This results in reduced network load and improved real time performance of the distributed system. Experiments were conducted to compare the performance of the proposed system with the performance of a centralized system. The results show that the distributed system uses less system resources and has better real time performance. What is more, this framework has been applied to Yaskawa's robot “SmartPal.” The simulation and experiment results show that our robotic framework can simulate and control the robot to perform complex tasks.
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Xu, Linfeng, Gang Li, Peiheng Song, and Weixiang Shao. "Vision-Based Intelligent Perceiving and Planning System of a 7-DoF Collaborative Robot." Computational Intelligence and Neuroscience 2021 (September 14, 2021): 1–25. http://dx.doi.org/10.1155/2021/5810371.
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In this paper, an intelligent perceiving and planning system based on deep learning is proposed for a collaborative robot consisting of a 7-DoF (7-degree-of-freedom) manipulator, a three-finger robot hand, and a vision system, known as IPPS (intelligent perceiving and planning system). The lack of intelligence has been limiting the application of collaborative robots for a long time. A system to realize “eye-brain-hand” process is crucial for the true intelligence of robots. In this research, a more stable and accurate perceiving process was proposed. A well-designed camera system as the vision system and a new hand tracking method were proposed for operation perceiving and recording set establishment to improve the applicability. A visual process was designed to improve the accuracy of environment perceiving. Besides, a faster and more precise planning process was proposed. Deep learning based on a new CNN (convolution neural network) was designed to realize intelligent grasping planning for robot hand. A new trajectory planning method of the manipulator was proposed to improve efficiency. The performance of the IPPS was tested with simulations and experiments in a real environment. The results show that IPPS could effectively realize intelligent perceiving and planning for the robot, which could realize higher intelligence and great applicability for collaborative robots.
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Muthuswamy, S., and S. Manoochehri. "Optimal Path Planning for Robot Manipulators." Journal of Mechanical Design 114, no. 4 (December 1, 1992): 586–95. http://dx.doi.org/10.1115/1.2917048.
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The study reported in this paper deals with a computer-based methodology for the synthesis of an optimal tool path for robot manipulators in the presence of obstacles and singularities of the workspace. The methodology plans optimal path to achieve the best robot kinematic and dynamic performance criteria formulated through proper objective functions. The algorithm uses robot design parameters, the size and the location of the obstacles, and the initial and the goal states to generate a collision-free optimal tool path. Using these inputs the robot workspace is generated and discretized, and the obstacles are modeled as forbidden regions of the workspace. The search for the optimal path begins with the definition of a searchspace that includes the starting and the end points. All possible paths in the searchspace connecting these points are enumerated through the formation of a network graph structure. An intelligent heuristic search scheme has been developed to enumerate the network of allowable paths. The optimal path is then obtained as a sequence of via points connecting the initial and the final states by applying Dijkstra’s minimum cost algorithm. Contrary to most existing methodologies, the computational complexity of this algorithm decreases with an increase in the number and/or the size of the obstacles in the workspace. An interactive computer program has been developed to implement this methodology for a general planar two-link manipulator. This path planning methodology can be applied to any manipulator for which the workspace and the obstacles can be geometrically represented. The algorithm has been applied to some industrial SCARA robots and the results are discussed.
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Best, Graeme, Oliver M. Cliff, Timothy Patten, Ramgopal R. Mettu, and Robert Fitch. "Dec-MCTS: Decentralized planning for multi-robot active perception." International Journal of Robotics Research 38, no. 2-3 (March 8, 2018): 316–37. http://dx.doi.org/10.1177/0278364918755924.
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We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
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Keerthana, V., C. Kiruthiga, P. Kiruthika, V. Sowmiya, and R. Manikadan. "NAVIGATION OF MOBILE ROBOT- ALGORITHM FOR PATH PLANNING & COLLISION AVOIDANCE- A REVIEW." International Journal of Research -GRANTHAALAYAH 5, no. 1 (January 31, 2017): 198–205. http://dx.doi.org/10.29121/granthaalayah.v5.i1.2017.1735.
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The field of autonomous mobile robotics has recently gained many researchers’ interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective of our project is applications based mobile robot systems, especially in navigation, designing real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective behind using the obstacle avoidance approach is to obtain a collision-free trajectory from the starting point to the target in monitoring environments. The ability of the robot to follow a path, detects obstacles, and navigates around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Motion planning that allows the robot to reach its target without colliding with any obstacles that may exist in its path.
To avoid collision in the mobile robot environment, providing a path planning& line following approach. Line following, path planning, collision avoidance, back propagation, improved memory, detecting long distance obstacles. Cheap and economical than the former one. Also work with back propagation technique.
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Van Pham, Hai, Farzin Asadi, Nurettin Abut, and Ismet Kandilli. "Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications." Applied Sciences 9, no. 9 (May 9, 2019): 1909. http://dx.doi.org/10.3390/app9091909.
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Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.
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Ćurković, Petar, and Lovro Čehulić. "Diversity Maintenance for Efficient Robot Path Planning." Applied Sciences 10, no. 5 (March 3, 2020): 1721. http://dx.doi.org/10.3390/app10051721.
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Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.
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Saleem Sumbal, Muhammad. "Environment Detection and Path Planning Using the E-puck Robot." IAES International Journal of Robotics and Automation (IJRA) 5, no. 3 (August 20, 2016): 151. http://dx.doi.org/10.11591/ijra.v5i3.pp151-160.
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Automatic path planning is one of the most challenging problems confronted by autonomous robots. Generating optimal paths for autonomous robots are some of the heavily studied subjects in mobile robotics applications. This paper documents the implementation of a path planning project using a mobile robot in a structured environment. The environment is detected through a camera and then a roadmap of the environment is built using some algorithms. Finally a graph search algorithm called A* is implemented that searches through the roadmap and finds an optimal path for robot to move from start position to goal position avoiding obstacles