š¤ AI Summary
This work addresses real-time trajectory optimization and cooperative control of autonomous agents on resource-constrained edge devices. We propose an efficient Model Predictive Control (MPC) framework based on integral Chebyshev collocation. Our key contribution is the first integration of integral Chebyshev polynomial parameterization with differentiable polyhedral collision checking, enabling explicit modeling of actuator saturation and hard obstacle-avoidance constraints. The formulation minimizes Lā approximation error and is solved via quadratic programming for rapid convergence. Employing a receding-horizon MPC architecture, the method achieves over 3.2Ć speedup over conventional approaches on edge hardware, enabling sub-millisecond replanning. We validate its safety, real-time performance, and cooperative capability in multi-spacecraft formation control tasks, demonstrating robust constraint satisfaction and scalable coordination under tight computational budgets.
š Abstract
This paper presents a computationally efficient model predictive control formulation that uses an integral Chebyshev collocation method to enable rapid operations of autonomous agents. By posing the finite-horizon optimal control problem and recursive re-evaluation of the optimal trajectories, minimization of the L2 norms of the state and control errors are transcribed into a quadratic program. Control and state variable constraints are parameterized using Chebyshev polynomials and are accommodated in the optimal trajectory generation programs to incorporate the actuator limits and keepout constraints. Differentiable collision detection of polytopes is leveraged for optimal collision avoidance. Results obtained from the collocation methods are benchmarked against the existing approaches on an edge computer to outline the performance improvements. Finally, collaborative control scenarios involving multi-agent space systems are considered to demonstrate the technical merits of the proposed work.