Path Planning for Spacecraft Obstacle Avoidance Based on Improved Sparrow Search Algorithm

With the rapid development of space technology, the complexity of space missions continues to increase. Tasks such as spacecraft formation flying, in orbit services, and rendezvous missions require spacecraft to perform higher-level autonomous motion planning. The presence of space debris and other operational spacecraft in orbit makes collision avoidance a key issue in spacecraft orbit planning. Sparrow Search Algorithm (SSA) is an emerging swarm intelligence optimization technology that has shown strong performance in solving complex optimization problems in recent years. In order to improve the optimization accuracy of the algorithm, this paper introduces the refraction reverse learning strategy and Cauchy mutation strategy to enhance the sparrow search algorithm, forming an improved optimization algorithm (RCSSA). This scheme can reduce diversity loss, decrease the probability of the algorithm falling into local optima, and improve the problem of insufficient convergence accuracy. Based on the above scheme, this paper conducts research on obstacle avoidance path planning for spacecraft, with the optimization objective of fuel optimization, to achieve obstacle avoidance work for spacecraft. By simulation analysis, the convergence accuracy of the algorithm has been improved. The optimization results meet the path planning conditions, verifying the feasibility of the algorithm.