Learning-based decentralized control with collision avoidance for multi-agent systems
In this paper, we present a learning-based tracking controller based on Gaussian processes (GP) for collision avoidance of multi-agent systems where the agents evolve in the special Euclidean group in the space SE(3). In particular, we use GPs to estimate certain uncertainties that appear in the dynamics of the agents. The control algorithm is designed to learn and mitigate these uncertainties by using GPs as a learning-based model for the predictions. In particular, the presented approach guarantees that the tracking error remains bounded with high probability. We present some simulation results to show how the control algorithm is implemented.
Code (0)
등록된 구현이 없습니다.
Tasks
Collision AvoidanceGaussian ProcessesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Contingency Model-based Control (CMC) for Communicationless Cooperative Collision Avoidance in Robot Swarms
Cooperative collision avoidance between robots, or `agents,' in swarm operations remains an open challenge. Assuming a decentralized architecture, each agent is responsible for making its own decisions and choosing its c…
Collision AvoidanceDecentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Most existing approaches either rely on conservative coordination mechan…
Collision AvoidanceNeural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using…
Collision AvoidanceTowards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While o…
Collision AvoidanceDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent reinforcement learning remains challenging both in its th…
Collision AvoidanceMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1