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Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning (IFAC WC 2023 and IV 2023)
We present a novel method in this work to address the problem of multi-vehicle conflict resolution in highly constrained spaces.Â
A high-fidelity optimal control problem is formulated to incorporate nonlinear, non-holonomic vehicle dynamics and exact collision avoidance constraints. Despite being high-dimensional and non-convex, we can obtain an optimal solution by learning configuration strategies with reinforcement learning (RL) in a simplified discrete environment and approaching high-quality initial guesses progressively.
The simulation results show that our method can explore efficient actions to resolve conflicts in confined space and generate dexterous maneuvers that are both collision-free and kinematically feasible.
Centralized Method
Centralized Method
Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning
Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning
Xu Shen, Francesco Borrelli
MPC Lab, Dept. of Mechanical Engineering, University of California, Berkeley, CA, USA(Accepted by IFAC World Congress 2023)
IFAC2023 2023-03-29.mp4Other Scenarios
Other Scenarios
Distributed Method
Distributed Method
Reinforcement Learning and Distributed Model Predictive Control for Conflict Resolution in Highly Constrained Spaces
Reinforcement Learning and Distributed Model Predictive Control for Conflict Resolution in Highly Constrained Spaces
Xu Shen, Francesco Borrelli
MPC Lab, Dept. of Mechanical Engineering, University of California, Berkeley, CA, USA(Accepted by IEEE Intelligent Vehicle Symposium 2023)
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