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.
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Updated
May 19, 2024 - C++
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.
This project develops multi-agent path planning algorithms for a dataset of pathfinding problems. The solution in C++ is accompanied with a visualization tool made in Python.
Multi agent path planning
CSintolSDK is the C++SDK for link and inveraction to SintolRTOSNode
[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.
Multiple implementations of boids
A GUI application for creating task assignment formations of NAO humanoid robots in SPL.
HierLearning is a C++11 implementation of a multi-agent, hierarchical reinforcement learning system for sequential decision problems.
Machine Learning and Guidance of Swarm in Dynamic and Partially Observable Environments
Multi-Agent Distributed Adaptive Resource Allocation
Evoplex is a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. It's available for Windows, Linux and macOS.
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…
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.
Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving
Trajectory Planner in Multi-Agent and Dynamic Environments
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