A Novel Framework for Trajectory Planning and Safe Navigation of Satellite Swarms

Abstract

This paper proposes a new framework for near-optimal, collision-free navigation of a swarm of identical satellites from an initial formation to a desired final formation. The framework combines a centralized optimization problem for location assignment with collision avoidance constraints, a decentralized fuel-optimal trajectory planner, a decentralized Artificial Potential Field (APF)-based real-time controller to ensure robustness, and a Shrinking Horizon Model Predictive Control logic to prevent deadlocks and livelocks and to improve robustness. To ensure high performance and low fuel cost, a novel optimization problem is formulated to tune the APF parameters based on mission requirements. The proposed framework provides a balance between optimality and scalability while keeping computational requirements low. The effectiveness of the frame work is demonstrated using numerical simulations.