A Rapid Trajectory Optimization and Control Framework for Resource-Constrained Applications

TL;DR

This paper introduces a fast, resource-efficient model predictive control framework using Chebyshev collocation for autonomous agents, enabling rapid trajectory optimization, collision avoidance, and multi-agent coordination on edge computers.

Contribution

It develops a novel Chebyshev collocation-based MPC method that efficiently handles constraints and collision avoidance for resource-constrained autonomous systems.

Findings

Achieves faster trajectory computation on edge hardware.

Effectively incorporates actuator and safety constraints.

Demonstrates improved performance in multi-agent scenarios.

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 keep-out 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.