Abstract:It is well-known that graph-based multi-robot path planning (MRPP) is NP-hard to optimally solve. In this work, we propose the first low polynomial-time algorithm for MRPP achieving 1-1.5 asymptotic optimality guarantees on solution makespan (i.e., the time it takes to complete a reconfiguration of the robots) for random instances under very high robot density, with high probability. Specifically, on an m1 ×m2 gird, m1 ≥ m2, our RTH (Rubik Table with Highways) algorithm computes solutions for routing up to m 1… Show more
“…3). Such configurations are called balanced configurations [47]. In practice, configurations are likely not "far" from being balanced.…”
Section: Methodsmentioning
confidence: 99%
“…The matching heuristic we developed is based on linear bottleneck assignment (LBA), which runs in polynomial time. A 2D version of the heuristic is introduced in [47], to which readers are referred to for more details. Here, we illustrate how to apply LBA-matching in MatchingXY.…”
Section: Improving Solution Quality Via Optimized Matchingmentioning
confidence: 99%
“…This research builds on [47], which addresses the MRPP problem in 2D. In comparison to [47], the current work describes a significant extension to the 3D (as well as higher dimensional) domain with many unique applications, including, e.g., the reconfiguration of large UAV fleets, the optimal coordination of air traffic, and the adjustment of satellite constellations.…”
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.
“…3). Such configurations are called balanced configurations [47]. In practice, configurations are likely not "far" from being balanced.…”
Section: Methodsmentioning
confidence: 99%
“…The matching heuristic we developed is based on linear bottleneck assignment (LBA), which runs in polynomial time. A 2D version of the heuristic is introduced in [47], to which readers are referred to for more details. Here, we illustrate how to apply LBA-matching in MatchingXY.…”
Section: Improving Solution Quality Via Optimized Matchingmentioning
confidence: 99%
“…This research builds on [47], which addresses the MRPP problem in 2D. In comparison to [47], the current work describes a significant extension to the 3D (as well as higher dimensional) domain with many unique applications, including, e.g., the reconfiguration of large UAV fleets, the optimal coordination of air traffic, and the adjustment of satellite constellations.…”
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.
It is well-known that graph-based multi-robot path planning (MRPP) is NP-hard to optimally solve. In this work, we propose the first low polynomial-time algorithm for MRPP achieving 1-1.5 asymptotic optimality guarantees on solution makespan (i.e., the time it takes to complete a reconfiguration of the robots) for random instances under very high robot density, with high probability. The dual guarantee on computational efficiency and solution optimality suggests our proposed general method is promising in significantly scaling up multi-robot applications for logistics, e.g., at large robotic warehouses.Specifically, on an m1 × m2 gird, m1 ≥ m2, our RTH (Rubik Table with Highways) algorithm computes solutions for routing up to m 1 m 2 3 robots with uniformly randomly distributed start and goal configurations with a makespan of m1 + 2m2 + o(m1), with high probability. Because the minimum makespan for such instances is m1 + m2 − o(m1), also with high probability, RTH guarantees m 1 +2m 2 m 1 +m 2 optimality as m1 → ∞ for random instances with up to 1 3 robot density, with high probability.Alongside this key result, we also establish a series of related results supporting even higher robot densities and environments with regularly distributed obstacles, which directly map to real-world parcel sorting scenarios. Building on the baseline methods with provable guarantees, we have developed effective, principled heuristics that further improve the computed optimality of the RTH algorithms. In extensive numerical evaluations, RTH and its variants demonstrate exceptional scalability as compared with methods including ECBS and DDM, scaling to over 450 × 300 grids with 45, 000 robots, and consistently achieves makespan around 1.5 optimal or better, as predicted by our theoretical analysis.
Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.
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