Optimization of Preemptive Impact Mitigation without Prior Collision Testing
Hayato Nakamura, Hikaru Arita, Shunsuke Tokiwa, Kenji Tahara
AI summary
Problem
Tuning impact mitigation parameters typically relies on trial-and-error collision experiments, which is impractical and risky due to the potential for damaging both the robot and its environment.
Approach
The authors model proximity sensor outputs using exponential functions to reformulate impact mitigation dynamics into analytically tractable equations, enabling direct optimization of control parameters without physical collision trials.
Key results
- Novel parameter design methodology eliminating preliminary collision trials
- First-order exponential model enabling exact analytical solutions
- Second-order exponential model providing superior approximation accuracy
- Validation through numerical simulations and experimental configurations
Why it matters
Enables safe, damage-free tuning of impact mitigation parameters for robots performing high-velocity or high-precision contact tasks.
Abstract
Effective impact mitigation strategies are crucial for preventing potential damage to both robotic systems and their operational environments during high-velocity and dynamic ma- neuvers, as well as during the execution of high-precision tasks. The successful implementation of impact mitigation strategies in real-world applications fundamentally requires appropriate pa- rameter tuning. However, owing to the destructive nature of col- lisions, heuristic parameter tuning is impractical, as it risks dam- age 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.