Relaxing Constraints in Anonymous Multi Agent Path Finding for Large Agents
Stepan Dergachev, Dmitry Avdeev

TL;DR
This paper improves an algorithm for anonymous multi-agent pathfinding in continuous space by relaxing the minimum separation constraints between agents, ensuring safety and goal achievement while allowing more flexible initial and goal configurations.
Contribution
It introduces a modification to existing AMAPF algorithms that reduces the minimum separation constraint from 4 radii to 2√3 radii, maintaining theoretical guarantees.
Findings
The modified algorithm preserves safety and goal achievement guarantees.
Reduced separation constraint from 4 to 2√3 radii.
Theoretical proof of property preservation.
Abstract
The study addressed the problem of Anonymous Multi-Agent Path-finding (AMAPF). Unlike the classical formulation, where the assignment of agents to goals is fixed, in the anonymous MAPF setting it is irrelevant which agent reaches specific goal, provided that all goals are occupied. Most existing multi-agent pathfinding algorithms rely on a discrete representation of the environment (e.g., square grids) and do not account for the sizes of agents. This limits their applicability in real-world scenarios, such as trajectory planning for mobile robots in warehouses. Conversely, methods operating in continuous space typically impose substantial restrictions on the input data, such as constraints on the distances between initial and goal positions or between start/goal positions and obstacles. In this work, we considered one of the AMAPF algorithms designed for continuous space, where agents…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Optimization and Search Problems · Computational Geometry and Mesh Generation
