An Efficient Multi-Agent Path Finding Solver for Car-Like Robots
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Updated
Jul 31, 2023 - C++
An Efficient Multi-Agent Path Finding Solver for Car-Like Robots
Continuous CBS - a modification of conflict based search algorithm, that allows to perform actions (move, wait) of arbitrary duration. Timeline is not discretized, i.e. is continuous.
Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried…
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)
Iterative Refinement for Real-Time Multi-Robot Path Planning (IROS-21)
📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
Multi-agent pathfinding via Conflict Based Search
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)
simple multi-agent pathfinding (MAPF) visualizer for research usage
Multi-Agent Pickup and Delivery implementation
Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding (IJCAI-23)
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO
Implementation of the SADG RHC feedback control scheme to reduce route completion times of delayed agents following MAPF plans.
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)
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