Impact-Robust Posture Optimization for Aerial Manipulation

TL;DR

This paper introduces a new impact-robust posture optimization method for redundant robots, enhancing safety and reducing post-impact spikes by integrating a configuration-dependent impact metric into a real-time control framework.

Contribution

It proposes a novel min-max optimization approach for impact robustness, reformulated as a gradient-based motion task embedded in a whole-body controller for real-time application.

Findings

Up to 51% reduction in post-impact spikes in aerial manipulator.

Successful integration within a real-time control framework.

Demonstrated impact robustness improvements on quadruped and humanoid robots.

Abstract

We present a novel method for optimizing the posture of kinematically redundant torque-controlled robots to improve robustness during impacts. A rigid impact model is used as the basis for a configuration-dependent metric that quantifies the variation between pre- and post-impact velocities. By finding configurations (postures) that minimize the aforementioned metric, spikes in the robot's state and input commands can be significantly reduced during impacts, improving safety and robustness. The problem of identifying impact-robust postures is posed as a min-max optimization of the aforementioned metric. To overcome the real-time intractability of the problem, we reformulate it as a gradient-based motion task that iteratively guides the robot towards configurations that minimize the proposed metric. This task is embedded within a task-space inverse dynamics (TSID) whole-body controller, enabling seamless integration with other control objectives. The method is applied to a kinematically redundant aerial manipulator performing repeated point contact tasks. We test our method inside a realistic physics simulator and compare it with the nominal TSID. Our method leads to a reduction (up to 51% w.r.t. standard TSID) of post-impact spikes in the robot's configuration and successfully avoids actuator saturation. Moreover, we demonstrate the importance of kinematic redundancy for impact robustness using additional numerical simulations on a quadruped and a humanoid robot, resulting in up to 45% reduction of post-impact spikes in the robot's state w.r.t. nominal TSID.