Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding

Okumura, Keisuke; Machida, Manao; Défago, Xavier; Tamura, Yasumasa

doi:10.24963/ijcai.2019/76

Cited by 44 publications

(39 citation statements)

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“…• 4-connected grids are a 32×32 map with 20% obstacles and an 8 × 8 map with 12% obstacles [31], [27], [35]. • Kiva-like map (22×53) is a warehouse map in the Kiva systems.…”

Section: Resultsmentioning

confidence: 99%

“…These algorithms can be broadly clas- sified into unbounded and bounded approaches. Unbounded sub-optimal solvers, such as Diversified-path and Databasedriven algorithm (DDM) [24], Cooperative A* (CA*) [25], Push and Rotate (PPR) [26], Priority Inheritance with Backtracking (PIBT) [27] and some learning based techniques [28], [29] generally aim to produce the solutions as fast as possible, often leading to large deviation from optimality.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

et al. 2022

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A Multi-Agent Path Finding (MAPF) problem involves multiple agents who want to reach their destinations without obstructing other agents. Although a MAPF problem needs to be solved for many real-world deployments, solving such a problem optimally is NP-hard. Many approaches have been proposed in the literature that offers sub-optimal solutions to this problem. For example, the Enhanced Conflict Based Search (ECBS) algorithm compromises the solution quality up to a constant factor to gain a notable runtime improvement. However, these algorithms use a fixed global sub-optimal bound for all agents, regardless of their preferences. In effect, with the increase in the number of agents, the runtime performance degrades. Against this backdrop, with the intent to further speed up the runtime, we propose an adaptive agent-specific sub-optimal bounding approach, called ASB-ECBS, that can be executed statically or dynamically. Specifically, ASB-ECBS can assign sub-optimal bound considering an individual agent's requirement. Additionally, we theoretically prove that the solution cost of ASB-ECBS remains within the sub-optimal bound. Finally, our extensive empirical results depict a notable improvement in the runtime by using ASB-ECBS while reducing the search space compared to the state-of-the-art MAPF algorithms.

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“…• 4-connected grids are a 32×32 map with 20% obstacles and an 8 × 8 map with 12% obstacles [31], [27], [35]. • Kiva-like map (22×53) is a warehouse map in the Kiva systems.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

et al. 2022

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show abstract

“…Multi-agent path finding (MAPF) is a problem of MAPP in discrete spaces, with the objective of finding a set of timed paths on graphs while preventing collisions with other agents [58]. MAPF has been studied extensively over the last decade and has produced numerous powerful algorithms (e.g., [4,34,45,51]). More recently, some works have attempted to leverage machine-learning techniques for solving MAPF.…”

Section: Related Workmentioning

confidence: 99%

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

Okumura¹,

Yonetani²,

Nishimura³

et al. 2022

Preprint

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Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximating the spaces, called roadmaps, and then apply multi-agent pathfinding (MAPF) algorithms to derive a set of conflict-free paths. While conventional studies have utilized roadmap construction methods developed for single-agent planning, it remains largely unexplored how we can construct roadmaps that work effectively for multiple agents. To this end, we propose a novel concept of roadmaps called cooperative timed roadmaps (CTRMs). CTRMs enable each agent to focus on its important locations around potential solution paths in a way that considers the behavior of other agents to avoid inter-agent collisions (i.e., "cooperative"), while being augmented in the time direction to make it easy to derive a "timed" solution path. To construct CTRMs, we developed a machine-learning approach that learns a generative model from a collection of relevant problem instances and plausible solutions and then uses the learned model to sample the vertices of CTRMs for new, previously unseen problem instances. Our empirical evaluation revealed that the use of CTRMs significantly reduced the planning effort with acceptable overheads while maintaining a success rate and solution quality comparable to conventional roadmap construction approaches. †

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“…Balancing the running-time and optimality is one of the most attractive topics in the study of MRPP/MAPF. Some algorithms emphasize the scalability without sacrificing as much optimality, e.g., ECBS [3], DDM [19], EECBS [27], PIBT [33], PBS [31]. Recently, O(1)−approximate or constant factor time-optimal algorithms have been proposed, e.g.…”

Section: Introductionmentioning

confidence: 99%

Sub-1.5 Time-Optimal Multi-Robot Path Planning on Grids in Polynomial Time

Yu¹

2022

Preprint

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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 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. m 1 +2m 2 m 1 +m 2 ∈ (1, 1.5]. Alongside the above-mentioned key result, we also establish:(1) for completely filled grids, i.e., m1m2 robots, any MRPP instance may be solved in polynomial time under a makespan of 7m1 + 14m2, (2) for m 1 m 2 3 robots, RTH solves arbitrary MRPP instances with makespan of 3m1 + 4m2 + o(m1), (3) for m 1 m 2 2 robots, a variation of RTH solves a random MRPP instance with the same 1-1.5 optimality guarantee, and (4) the same m 1 +2m 2 m 1 +m 2 optimality guarantee holds for regularly distributed obstacles at 1 9 density together with 2m 1 m 2 9 randomly distributed robots; such settings directly map to real-world parcel sorting scenarios.Moreover, we have developed effective, principled heuristics that further improve the computed optimality of 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.• The same m1+2m2 m1+m2 optimality guarantee may be achieved on grids with up to m1m2 9 regularly distributed obstacles together with 2m1m2 9 robots using RTH (e.g., Fig. 1(b)).

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Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding

Cited by 44 publications

References 0 publications

An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

Sub-1.5 Time-Optimal Multi-Robot Path Planning on Grids in Polynomial Time

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