There are 12 repositories under mapf topic.
Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
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.
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.
This is a multi-agent path planning(also known as Multi-Agent Path Finding, MAPF) algorithm package for ROS
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 out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.
📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
simple multi-agent pathfinding (MAPF) visualizer for research usage
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)
Multi-Agent Pickup and Delivery implementation
[AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.
"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. 🤖🛤️
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO
[AAAI-2024] MATS-LP addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed approach utilizes a combination of Monte Carlo Tree Search and reinforcement learning for resolving conflicts.
Implementation of the SADG RHC feedback control scheme to reduce route completion times of delayed agents following MAPF plans.
Conflict Avoidant CBS (CA-CBS)
code corresponding to a research paper about M* (M star) and it's modification to solve multi agent pathfinding with matching problems (MMAPF)
Explainable Multi-Agent Path Finder (XMAPF, work in progress) based on Lazy Conflict-Based Search (Gange, Harabor and Stuckey, 2021).
Combinatorial Decision Making and Optimization Course Project.
Multi Agent Path Finding (MAPF) advanced solver infrastructure with Bi-Directional solver and automated splitting method
This is the repo for the team Pikachu's solution in the League of Robot Competition 2023. Our solution won the Overall Best and Fast Mover tracks and ranked second in the Line Honours track.
[AAMAS 2024] HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding