SEAR: A Polynomial- Time Multi-Robot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee

Han, Shuai D.; Rodriguez, Edgar; Yu, Jingjin

doi:10.1109/iros.2018.8594417

Cited by 13 publications

(6 citation statements)

References 51 publications

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“…We assume the grid size is m 1 × m 2 × m 3 and there are m1m2m3 3 robots. For density less than 1 3 , we add virtual robots [46], [49] till reaching 1 3 . A. RTH3D: Adapting RTA3D for MRPP in 3D Algorithm 1 outlines the high-level process for multi-robot routing coordination in 3D.…”

Section: Methodsmentioning

confidence: 99%

Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination

Feng¹,

Yu²

2022

Preprint

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For enabling efficient, large-scale coordination of unmanned aerial vehicles (UAVs) under the labeled setting, in this work, we develop the first polynomial time algorithm for the reconfiguration of many moving bodies in three-dimensional spaces, with provable 1.x asymptotic makespan optimality guarantee under high robot density. More precisely, on an m1 × m2 × m3 grid, m1 ≥ m2 ≥ m3, our method computes solutions for routing up to m 1 m 2 m 3 3 uniquely labeled robots with uniformly randomly distributed start and goal configurations within a makespan of m1 + 2m2 + 2m3 + o(m1), with high probability. Because the makespan lower bound for such instances is m1 + m2 + m3 − o(m1), also with high probability, as m1 → ∞, m 1 +2m 2 +2m 3 m 1 +m 2 +m 3 optimality guarantee is achieved.3 ], yielding 1.x optimality. In contrast, it is well-known that multi-robot path planning is NP-hard to optimally solve. In numerical evaluations, our method readily scales to support the motion planning of over 100, 000 robots in 3D while simultaneously achieving 1.x optimality. We demonstrate the application of our method in coordinating many quadcopters in both simulation and hardware experiments.

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Section: Methodsmentioning

confidence: 99%

Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination

Feng¹,

Yu²

2022

Preprint

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“…These items are eventually to be manged by other parts in the system. For example, with proper synchronization and feedback based control, solutions generated by the ILP-based MPP solver [10] can be readily executed on multi-robot hardware platforms [45].…”

Section: B Integer Programming Basicsmentioning

confidence: 99%

Integer Programming as a General Solution Methodology for Path-Based Optimization in Robotics: Principles, Best Practices, and Applications

Han

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Integer programming (IP) has proven to be highly effective in solving many path-based optimization problems in robotics. However, the applications of IP are generally done in an ad-hoc, problem specific manner. In this work, after examined a wide range of path-based optimization problems, we describe an IP solution methodology for these problems that is both easy to apply (in two simple steps) and high-performance in terms of the computation time and the achieved optimality. We demonstrate the generality of our approach through the application to three challenging path-based optimization problems: multi-robot path planning (MPP), minimum constraint removal (MCR), and reward collection problems (RCPs). Associated experiments show that the approach can efficiently produce (near-)optimal solutions for problems with large state spaces, complex constraints, and complicated objective functions. In conjunction with the proposition of the IP methodology, we introduce two new and practical robotics problems: multi-robot minimum constraint removal (MMCR) and multi-robot path planning (MPP) with partial solutions, which can be quickly and effectively solved using our proposed IP solution pipeline.

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“…Besides, every robot should abide by motion constraints [2] and have the ability to cope with unexpected events in obstacle-strewn environment. CPPMR has been proved to be a NP-hard problem in [1], [3], and seeking efficient schemes is always the research hot topic in recent decades.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth exploring to apply the approach suitable for a single robot to multiple robots [20]. A* planner [21] is used as the fundamental planner in [3], [22]. Then the enhanced partial expansion and the subdimensional expansion are respectively introduced to solve CPPMR.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Path Planning for Multiple Robots With Motion Constraints in Obstacle-Strewn Environment

Shao

Luo

et al. 2019

IEEE Access

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A cooperative path searching approach is proposed to decouple cooperative path planning for multiple robots into the path planning phase and the trajectory tracking phase. Unexpected local environment changes or failures for some robots will not affect the regular running of other robots. The collision between robots and the motion constraints of robots are not considered in the first phase. A new connection point method suitable for the trap-like map is used to find the shortest path for every robot. Connection point method does not require much computation cost even if the grid map is zoomed in. In the second phase, a cooperative search tracking approach based on modified coevolution pigeon-inspired optimization algorithm is proposed to enable every robot to track the grid path obtained by the connection point method.A competition mechanism with serial number priority is used to cope with collisions between robots. The numerical simulations are performed to verify the effectiveness of the proposed approach.INDEX TERMS Cooperative path planning for multiple robots, cooperative search tracking, motion constraints, shortest path, modified coevolution pigeon-inspired optimization.

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SEAR: A Polynomial- Time Multi-Robot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee

Cited by 13 publications

References 51 publications

Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination

Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination

Integer Programming as a General Solution Methodology for Path-Based Optimization in Robotics: Principles, Best Practices, and Applications

Cooperative Path Planning for Multiple Robots With Motion Constraints in Obstacle-Strewn Environment

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