NMPC for Collision Avoidance by Superellipsoid Separation

TL;DR

This paper presents a real-time NMPC method for obstacle avoidance in heavy equipment using superellipsoid models and optimization techniques, enabling efficient navigation in complex environments.

Contribution

It introduces a novel NMPC formulation that models vehicles and obstacles as superellipsoids, integrating the separating hyperplane theorem and OpEn for improved obstacle avoidance.

Findings

Successful simulation and experimental validation

Effective obstacle avoidance in obstructed environments

Real-time capability demonstrated on skid-steer loader

Abstract

This paper introduces a novel NMPC formulation for real-time obstacle avoidance on heavy equipment by modeling both vehicle and obstacles as convex superellipsoids. The combination of this approach with the separating hyperplane theorem and Optimization Engine (OpEn) allows to achieve efficient obstacle avoidance in autonomous heavy equipment and robotics. We demonstrate the efficacy of the approach through simulated and experimental results, showcasing a skid-steer loader's capability to navigate in obstructed environments.