Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
This work addresses efficient and safe coordination in multi-agent systems, such as autonomous vehicles, with incremental improvements in decentralized control.
The paper tackles trajectory optimization for nonlinear multi-agent systems by proposing a decentralized learning model predictive control scheme that uses data from previous tasks to improve local safe sets and value functions, resulting in persistent feasibility, finite-time convergence, and non-decreasing performance, as demonstrated in multi-vehicle collision avoidance experiments.
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task iteratively, data from previous task executions is used to construct and improve local time-varying safe sets and an approximate value function. These are used in a decoupled MPC problem as terminal sets and terminal cost functions. Our framework results in a decentralized controller, which requires no communication between agents over each iteration of task execution, and guarantees persistent feasibility, finite-time closed-loop convergence, and non-decreasing performance of the global system over task iterations. Numerical experiments of a multi-vehicle collision avoidance scenario demonstrate the effectiveness of the proposed scheme.