Abstract
This paper introduces a novel nonlinear model predictive controller (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.
Issue Section:
Research Papers
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