The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Manuela Veloso - One of the best experts on this subject based on the ideXlab platform.
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real time randomized Path Planning for robot navigation
Lecture Notes in Computer Science, 2003Co-Authors: James R Bruce, Manuela VelosoAbstract:Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot's capabilities. This poses the problem of Planning a Path through a continuous domain. Several approaches have been used to address this problem each with some limitations, including state discretizations, Planning efficiency, and lack of interleaved execution. Rapidly-exploring random trees (RRTs) are a recently developed algorithm on which fast continuous domain Path planners can be based. In this work, we build a Path Planning system based on RRTs that interleaves Planning and execution, first evaluating it in simulation and then applying it to physical robots. Our algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost search, which improve rePlanning efficiency and the quality of generated Paths. ERRT is successfully applied to a multi-robot system. Results demonstrate that ERRT is improves efficiency and performs competitively with existing heuristic and reactive real-time Path Planning approaches. ERRT has shown to offer a major step with great potential for Path Planning in challenging continuous, highly dynamic domains.
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real time randomized Path Planning for robot navigation
Intelligent Robots and Systems, 2002Co-Authors: James R Bruce, Manuela VelosoAbstract:Mobile robots often must find a trajectory to another position in their environment, subject to constraints. This is the problem of Planning a Path through a continuous domain Rapidly-exploring random trees (RRTs) are a recently developed representation on which fast continuous domain Path planners can be based. In this work, we build a Path Planning system based on RRTs that interleaves Planning and execution, first evaluating it in simulation and then applying it to physical robots. Our Planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve rePlanning efficiency and the quality of generated Paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for rePlanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time Path Planning approaches. ERRT is a significant step forward with the potential for making Path Planning common on real robots, even in challenging continuous, highly dynamic domains.
Morteza Aliyari - One of the best experts on this subject based on the ideXlab platform.
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mobile robots Path Planning electrostatic potential field approach
Expert Systems With Applications, 2018Co-Authors: Farhad Bayat, Sepideh Najafinia, Morteza AliyariAbstract:Abstract This paper deals with the mobile robots Path Planning problem in the presence of scattered obstacles in a visually known environment. Utilizing the theory of charged particles’ potential fields and inspired by a key idea of the authors’ recent work, an optimization based approach is proposed to obtain an optimal and robust Path Planning solution. By assigning a potential function for each individual obstacle, the interaction of all scattered obstacles are integrated in a scalar potential surface (SPS) which strongly depends on the physical features of the mobile robot and obstacles. The optimum Path is then obtained from a cost function optimization by attaining a trade-off between traversing the shortest Path and avoiding collisions, simultaneously. Hence, irrespective of any physical constraints on the obstacles/mobile-robot and the adjacency of the target to the obstacles, the achieved results demonstrate a feasible, fast, oscillation-free and collision-free Path Planning of the proposed method. Utilizing a scalar decision variable makes it extremely simple in terms of mathematical computations and thus practically feasible that can be applied to both static and dynamic environments. Finally, simulation results verified the performance and fulfillment of the mentioned objectives of the approach.
Howie Choset - One of the best experts on this subject based on the ideXlab platform.
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constraint manifold subsearch for multirobot Path Planning with cooperative tasks
International Conference on Robotics and Automation, 2015Co-Authors: Glenn Wagner, Jae Il Kim, Konrad Urban, Howie ChosetAbstract:The cooperative Path Planning problem seeks to determine a Path for a group of robots which form temporary teams to perform tasks that require multiple robots. The multi-scale effects of simultaneously coordinating many robots distributed across the workspace while also tightly coordinating robots in cooperative teams increases the difficulty of Planning. This paper describes a new approach to cooperative Path Planning called Constraint Manifold Subsearch (CMS). CMS builds upon M*, a high performance multirobot Path Planning algorithm, by modifying the search space to restrict teams of robots performing a task to the constraint manifold of the task. CMS can find optimal solutions to the cooperative Path Planning problem, or near optimal solutions to problems involving large numbers of robots.
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subdimensional expansion for multirobot Path Planning
Artificial Intelligence, 2015Co-Authors: Glenn Wagner, Howie ChosetAbstract:Abstract Planning optimal Paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot Path Planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during Planning by a robot–robot collision, the dimensionality of the search space is locally increased to ensure that an alternative Path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a Path. We present the M ⁎ algorithm, an implementation of subdimensional expansion that adapts the A ⁎ planner to perform efficient multirobot Planning. M ⁎ is proven to be complete and to find minimal cost Paths. Simulation results are presented that show that M ⁎ outperforms existing optimal multirobot Path Planning algorithms.
Shuchuan Chu - One of the best experts on this subject based on the ideXlab platform.
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a parallel compact cuckoo search algorithm for three dimensional Path Planning
Applied Soft Computing, 2020Co-Authors: Peicheng Song, Jengshyang Pan, Shuchuan ChuAbstract:Abstract The three-dimensional (3D) Path Planning of unmanned robots focuses on avoiding collisions with obstacles and finding an optimized Path to the target location in a complex three-dimensional environment. An improved cuckoo search algorithm based on compact and parallel techniques for three-dimensional Path Planning problems is proposed. This paper implements the compact cuckoo search algorithm, and then, a new parallel communication strategy is proposed. The compact scheme can effectively save the memory of the unmanned robot. The parallel scheme can increase the accuracy and achieve faster convergence. The proposed algorithm is tested on several selected functions and three-dimensional Path Planning. Results compared with other methods show that the proposed algorithm can provide more competitive results and achieve more efficient execution.
James R Bruce - One of the best experts on this subject based on the ideXlab platform.
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real time randomized Path Planning for robot navigation
Lecture Notes in Computer Science, 2003Co-Authors: James R Bruce, Manuela VelosoAbstract:Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot's capabilities. This poses the problem of Planning a Path through a continuous domain. Several approaches have been used to address this problem each with some limitations, including state discretizations, Planning efficiency, and lack of interleaved execution. Rapidly-exploring random trees (RRTs) are a recently developed algorithm on which fast continuous domain Path planners can be based. In this work, we build a Path Planning system based on RRTs that interleaves Planning and execution, first evaluating it in simulation and then applying it to physical robots. Our algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost search, which improve rePlanning efficiency and the quality of generated Paths. ERRT is successfully applied to a multi-robot system. Results demonstrate that ERRT is improves efficiency and performs competitively with existing heuristic and reactive real-time Path Planning approaches. ERRT has shown to offer a major step with great potential for Path Planning in challenging continuous, highly dynamic domains.
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real time randomized Path Planning for robot navigation
Intelligent Robots and Systems, 2002Co-Authors: James R Bruce, Manuela VelosoAbstract:Mobile robots often must find a trajectory to another position in their environment, subject to constraints. This is the problem of Planning a Path through a continuous domain Rapidly-exploring random trees (RRTs) are a recently developed representation on which fast continuous domain Path planners can be based. In this work, we build a Path Planning system based on RRTs that interleaves Planning and execution, first evaluating it in simulation and then applying it to physical robots. Our Planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve rePlanning efficiency and the quality of generated Paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for rePlanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time Path Planning approaches. ERRT is a significant step forward with the potential for making Path Planning common on real robots, even in challenging continuous, highly dynamic domains.
