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

Rahman, Mustafizur; Alam, Md. Ahasanul; Islam, Md Mofijul; Iqbal, Tarıq; Rahman, Ismat; Khan, Md. Mosaddek

doi:10.1109/access.2022.3151092

Cited by 8 publications

(7 citation statements)

References 33 publications

(28 reference statements)

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“…Significantly improves the runtime while reducing the search space [206] 4.2. Distributed Planning 4.2.1.…”

Section: Ecbsmentioning

confidence: 99%

Path Planning Technique for Mobile Robots: A Review

Yang,

Li,

Qian

et al. 2023

Machines

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Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

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“…Significantly improves the runtime while reducing the search space [206] 4.2. Distributed Planning 4.2.1.…”

Section: Ecbsmentioning

confidence: 99%

Path Planning Technique for Mobile Robots: A Review

Yang,

Li,

Qian

et al. 2023

Machines

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“…Some studies [12], [22], [30] have improved the scalability of an MAPF solver up to hundreds of agents while providing a near-optimal solution. Barer et al [12] proposed a bounded-suboptimal variant of CBS called enhanced CBS (ECBS) to improve the scalability of CBS.…”

Section: B Mapf Solvermentioning

confidence: 99%

“…Therefore, ECBS allows users to control the trade-off between scalability and solution quality through the suboptimality factor. Rahman et al [30] further reduced the computation time of the ECBS by applying different suboptimal bounds to each agent.…”

Section: B Mapf Solvermentioning

confidence: 99%

Anytime Lifelong Multi-Agent Pathfinding in Topological Maps

Song

2023

IEEE Access

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This study addresses a lifelong multi-agent path finding (lifelong MAPF) problem that continuously solves an MAPF instance online according to newly assigned goals. Specifically, we focus on lifelong MAPF in a topological map. This problem is challenging because the movement of the agent is restricted to narrow corridors in a topological map, rather than the entire map area. Bypasses may be limited or farther away in corridors, significantly complicating the computation of paths. Furthermore, low-quality solutions may cause traffic congestion or even deadlock in a corridor. Therefore, we propose a novel lifelong MAPF method that effectively resolves conflicts in corridors based on an anytime strategy. This method gradually improves the solution quality by updating sub-paths with heavy traffic congestion. Furthermore, we adopt several improvement steps to effectively resolve corridor conflicts in a conflict-based search (CBS). This method significantly reduces the search space and computation time of CBS. We conducted extensive experiments on various topological maps in warehouse and railway environments. The results show that the proposed method outperforms state-of-the-art methods in terms of throughput and success rate. In particular, the proposed method can resolve collisions with a longer time horizon than existing methods, considerably improving throughput on a topological map with long-range corridors and heavy traffic congestion.INDEX TERMS Multi-agent pathfinding, mobile robots, logistics automation, path planning, multi-robot system, topological map.

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“…8 Other approaches have proposed to incorporate the preferences of a decision maker over the multi-agent planning problem, 9 and have developed adaptive methods that can bound the sub-optimal cost of the solution. 10 The problem is also referred to as multi agent path finding (MAPF) and hierarchical approaches have been developed where a spatial hierarchy to partition the environment into smaller sub-instances is used, and bounded-suboptimal MAPF solvers are then applied to each sub-instance. 11 Other methods have proposed hierarchical parallel solvers for computing paths efficiently for groups of agents.…”

Section: Multi-agent Path Planningmentioning

confidence: 99%

Spatially distributed lane planning for navigation in 3D environments

Sharma

Kallmann

2023

Computer Animation & Virtual

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This article introduces the new problem of planning spatially distributed lanes for supporting multi‐agent navigation applications in 3D environments. Our proposed approach computes the max‐flow of a 3D medial axis representation of the environment in order to globally compute collision‐free lanes exploring the entire free space of the volumetric scene. Our method addresses agent clearance and path dispersion in order to provide a comprehensive solution to globally compute lanes to be used by multiple agents in 3D environments. By selecting the desired lane dispersion our approach offers an intuitive and powerful way to explore variations in the computed collections of lanes. Dispersion is addressed with a combination of new techniques based on max flow computation, clearance‐based path separation, and adaptive shortcut‐based smoothing.

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

Cited by 8 publications

References 33 publications

Path Planning Technique for Mobile Robots: A Review

Path Planning Technique for Mobile Robots: A Review

Anytime Lifelong Multi-Agent Pathfinding in Topological Maps

Spatially distributed lane planning for navigation in 3D environments

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