Fast Motion Planning for Agile Space Systems with Multiple Obstacles

Abstract

In this paper, we develop a novel algorithm for spacecraft trajectory planning in an environment cluttered with many geometrically-fixed obstacles. The Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm first uses a spherical-expansion-based sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory within the homotopy class using sequential convex programming. If the number of samples tends to infinity, then the SE-SCP trajectory converges to the globally optimal trajectory in the workspace. The SE-SCP algorithm is computationally efficient, therefore it can be used for real-time applications on resource-constrained systems. We also present results of numerical simulations and comparisons with existing algorithms.