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

TL;DR

The paper tackles non-convex collision avoidance in optimization-based trajectory generation for autonomous on-orbit assembly by representing each object as a union of convex sets and deriving differentiable convex distance constraints from the optimality conditions of a convex subproblem. This reformulation yields differentiable constraints that can be embedded in a gradient-based optimal control problem, applicable to both point-mass and full-dimensional vehicles. Key contributions include the convex modeling of sets via differentiable functions, the KKT-based distance constraints for point-mass and full-dimensional cases, and the demonstration of non-conservative, optimal trajectories in two tight AO scenarios using a pseudospectral solver. The approach has broad potential beyond AOA, enabling efficient collision avoidance in high-dimensional motion planning where proximity and close-pass maneuvers are critical, and it opens avenues for real-time planning and cross-domain applicability.

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.

Figures (9)

• Figure 1: Robotic Spacecraft
• Figure 2: Robotic Spacecraft Trajectory In Close Proximity of Target Structure - Scenario A: Red Object (Spacecraft) - Black Objects (Target Structure)
• Figure 3: States Time History - Scenario A
• Figure 4: Control Inputs Time History - Scenario A
• Figure 5: Lower Hamiltonian Time History