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
Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
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.
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.
Iterative Refinement for Real-Time Multi-Robot Path Planning (IROS-21)
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…
📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)
simple multi-agent pathfinding (MAPF) visualizer for research usage
Multi-agent pathfinding via Conflict Based Search
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)
Multi-Agent Pickup and Delivery implementation
Offline Time-Independent Multi-Agent Path Planning (IJCAI-22, T-RO-23)
Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding (IJCAI-23)
Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO
"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️
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