Non-Conservative Obstacle Avoidance for Multi-Body Systems Leveraging Convex Hulls and Predicted Closest Points

TL;DR

This paper presents a new collision avoidance method for multi-body systems that uses convex hulls and predicted closest points to improve safety, smoothness, and accuracy in obstacle-rich environments.

Contribution

It introduces a novel integration of future closest point predictions into collision avoidance constraints, addressing abrupt shifts and improving performance.

Findings

Enhanced collision risk reduction and smoother trajectories.

Improved distance prediction accuracy in simulations and experiments.

Safer navigation near obstacles in practical applications.

Abstract

This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.