Optimization of Preemptive Impact Mitigation Without Prior Collision Testing

Abstract

Effective impact mitigation strategies are crucial for preventing potential damage to both robotic systems and their operational environments during high-velocity and dynamic maneuvers, as well as during the execution of high-precision tasks. The successful implementation of impact mitigation strategies in real-world applications fundamentally requires appropriate parameter tuning. However, owing to the destructive nature of collisions, heuristic parameter tuning is impractical, as it risks damage to both the robotic system and its operational environment during experimental trials. This study eliminates the need for preliminary collision experiments in parameter optimization by introducing a novel methodology that leverages recent proximity sensor-based preemptive impact mitigation strategies that reframe impact mitigation as a geometric rather than physical problem. The key innovation of this work lies in the reformulation of the proximity sensor output to enable both the analytical derivation of preemptive motion trajectories and the direct application of standard optimization solvers. The effectiveness of the proposed methodology is validated through numerical simulations and two different experimental configurations. By eliminating the need for collision trials, robotic systems can safely execute potentially destructive tasks that would otherwise result in system damage without proper impact mitigation.