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arXiv · 2404.14257

NMPC for Collision Avoidance by Superellipsoid Separation

Ruairi Moran·Sheila Bagley·Seth Kasmann·Rob Martin·David Pasley·Shane Trimble·James Dianics·Pantelis Sopasakis
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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.

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Ruairi Moran, Sheila Bagley, Seth Kasmann, Rob Martin, David Pasley, Shane Trimble, James Dianics, Pantelis Sopasakis. 2024-04-22. NMPC for Collision Avoidance by Superellipsoid Separation. https://doi.org/10.1115/1.4066388

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