Fast motion planning for multiple moving robots

Buckley, Stephen J.

doi:10.1109/robot.1989.100008

Cited by 135 publications

(78 citation statements)

References 5 publications

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“…Priority planners assign a priority to each robot, then plan for individual robots in decreasing order of priority, treating higher priority robots as moving obstacles [4,35,36,37,38,39]. The choice of priority ordering is critical, leading to a number of heuristics for choosing priority orders that are likely to lead to a solution [38,40,41]. Turpin et al [42,43] showed that for permutation invariant multirobot path planning, where any robot can move to any goal location, there is a polynomial time algorithm for choosing an assignment of robots to goals and a priority order that is guaranteed to produce a solution.…”

Section: Decoupled Multirobot Path Planningmentioning

confidence: 99%

Subdimensional expansion for multirobot path planning

Wagner

Choset

2015

Artificial Intelligence

316

256

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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 robotrobot 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.

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Section: Decoupled Multirobot Path Planningmentioning

confidence: 99%

Subdimensional expansion for multirobot path planning

Wagner

Choset

2015

Artificial Intelligence

316

256

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“…The choice of the priorities can have a large impact on the performance of the algorithm [27]. Some planners also search through a space of prioritizations [3,4].…”

Section: Related Workmentioning

confidence: 99%

Centralized path planning for multiple robots: Optimal decoupling into sequential plans

Berg¹,

Snoeyink²,

Manocha

2009

Robotics: Science and Systems V

140

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Abstract-We develop an algorithm to decouple a multi-robot path planning problem into subproblems whose solutions can be executed sequentially. Given an external path planner for general configuration spaces, our algorithm finds an execution sequence that minimizes the dimension of the highest-dimensional subproblem over all possible execution sequences. If the external planner is complete (at least up to this minimum dimension), then our algorithm is complete because it invokes the external planner only for spaces of dimension at most this minimum. Our algorithm can decouple and solve path planning problems with many robots, even with incomplete external planners. We show scenarios involving 16 to 65 robots, where our algorithm solves planning problems of dimension 32 to 130 using a PRM planner for at most eight dimensions.

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“…For this reason, prioritisation of the tasks is a popular approach in ensuring the tasks of both manipulators can be completed successfully (Buckley, 1989).…”

Section: Introductionmentioning

confidence: 99%

Improved APF strategies for dual-arm local motion planning

Byrne

Naeem

Ferguson

2014

Transactions of the Institute of Measurement and Control

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Manipulator motion planning is a classic problem in robotics, with a number of complete solutions available for their motion in controlled (industrial) environments. Due to recent technological advances in the field of robotics, there has been a significant development of more complex robots with high fidelity sensors and more computational power. One such example has been a rise in the production of humanoid robots equipped with dual-arm manipulators which require complex motion planning algorithms. Also, the technological advances have resulted in a shift from using manipulators in strictly controlled environments, to investigating the deployment of manipulators in dynamic or unknown environments. As a result, a greater emphasis has been put on the development of local motion planners, which can provide real-time solutions to these problems. Artificial Potential Fields (APF) is one such popular local motion planning technique, which can be applied to manipulator motion planning, however, the basic algorithm is severely prone to local minima problems. Here, two modified APF-based strategies for solving the dual-arm motion planning task in unknown environments are proposed. Both techniques make use of configuration sampling and subgoal selection to assist the APF in avoiding these local minima scenarios.Extensive simulation results are presented to validate the efficacy of the proposed methodology.

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Fast motion planning for multiple moving robots

Cited by 135 publications

References 5 publications

Subdimensional expansion for multirobot path planning

Subdimensional expansion for multirobot path planning

Centralized path planning for multiple robots: Optimal decoupling into sequential plans

Improved APF strategies for dual-arm local motion planning

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