A Novel Optimization-Based Collision Avoidance For Autonomous On-Orbit Assembly

TL;DR

This paper introduces a novel convex optimization-based collision avoidance method for autonomous on-orbit assembly, enabling the generation of optimal, non-conservative trajectories in tight environments for robotic spacecraft.

Contribution

It proposes a new convex optimization framework that models collision avoidance constraints as convex functions, improving trajectory planning for space robotics.

Findings

Effective in tight assembly environments

Produces optimal collision-free trajectories

Applicable to generic motion planning problems

Abstract

The collision avoidance constraints are prominent as non-convex, non-differentiable, and challenging when defined in optimization-based motion planning problems. To overcome these issues, this paper presents a novel non-conservative collision avoidance technique using the notion of convex optimization to establish the distance between robotic spacecraft and space structures for autonomous on-orbit assembly operations. The proposed technique defines each ellipsoidal- and polyhedral-shaped object as the union of convex compact sets, each represented non-conservatively by a real-valued convex function. Then, the functions are introduced as a set of constraints to a convex optimization problem to produce a new set of differentiable constraints resulting from the optimality conditions. These new constraints are later fed into an optimal control problem to enforce collision avoidance where the motion planning for the autonomous on-orbit assembly takes place. Numerical experiments for two assembly scenarios in tight environments are presented to demonstrate the capability and effectiveness of the proposed technique. The results show that this framework leads to optimal non-conservative trajectories for robotic spacecraft in tight environments. Although developed for autonomous on-orbit assembly, this technique could be used for any generic motion planning problem where collision avoidance is crucial.