Simultaneous Control and Trajectory Estimation for Collision Avoidance of Autonomous Robotic Spacecraft Systems

TL;DR

This paper introduces a factor graph optimization approach for real-time planning, control, and trajectory estimation to enable collision-free navigation of autonomous spacecraft in dynamic environments.

Contribution

It presents a novel online probabilistic motion planning and trajectory estimation method that handles both known and unknown obstacle motions for space missions.

Findings

Effective collision avoidance in dynamic environments

Supports autonomous space mission navigation

Handles both known and unknown obstacle trajectories

Abstract

We propose factor graph optimization for simultaneous planning, control, and trajectory estimation for collision-free navigation of autonomous systems in environments with moving objects. The proposed online probabilistic motion planning and trajectory estimation navigation technique generates optimal collision-free state and control trajectories for autonomous vehicles when the obstacle motion model is both unknown and known. We evaluate the utility of the algorithm to support future autonomous robotic space missions.