An Autonomous Satellite Collision Avoidance and Adversary Evasion Path Planning Algorithm for the Space Environment
Cameron Mehlman, Gregory J. Falco · 2024
There have been numerous different proposed path planning algorithms capable of computing obstacle avoidance paths for autonomous vehicles. However, methods often fall short for path planning in six degrees of freedom (6dof) and dynamic environments such as those encountered by spacecraft in orbit. This article proposes the novel Enumerated Vectors for Autonomy in Dynamic Environments (EVADE) method - strongly influenced by the Vector Field Histogram (VFH) algorithm - which generates a desired path in 6dof environments for autonomous obstacle or adversary avoidance for space vehicles. EVADE's method of state representation converts large point cloud data sets obtained from LiDAR sensors into a series of Gaussian distributions which are stored in a 3D polar grid. EVADE's representation allows for a seamless analysis of the surrounding state in 3 dimensions, as well as propagation of obstacle states in environments with dynamic obstacles. EVADE is also performant in scenarios with intelligent dynamic obstacles that intentionally and continuously interfere with the planned path. In result, EVADE is capable of providing complex 3D spline paths to inform a space vehicle's guidance, navigation and control system while using minimal compute to enable edge-based avoidance maneuvers.