Rapidly Sampling-Based Trajectory Planning for Spacecraft Proximity

A suitably automatic rapid planning approach based on sampling-based algorithms is investigated for impulsive spacecraft closely proximity. Integrated with deterministic sampling and rewiring strategies, the enhanced sweeping Rapidly-Exploring Dense Trees (sweep-RDT*) are lazily extracted from the graph-based roadmaps for generating an asymptotically optimal trajectory, which is then smoothed using linearly homotopic superposition. The proposed method is finally validated and analyzed through various numerical simulations with different sample counts as well as exploring cost thresholds. Results indicate the ability for rapidly safe trajectory planning in agile spacecraft proximity scenarios.