Impact-Robust Posture Optimization for Aerial Manipulation

2026-02-14

AI Summary

This work proposes a novel approach that integrates an impact robustness metric with real-time whole-body control to enhance the postural robustness of aerial manipulation robots during collisions. Leveraging a rigid impact model, the method formulates a configuration-dependent impact sensitivity measure and casts robust posture selection as a min–max optimization problem. This problem is then transformed into a gradient-driven motion task embedded within a Task-Space Inverse Dynamics (TSID) controller, which actively exploits kinematic redundancy to optimize robot configuration and suppress transient impact responses. The proposed framework achieves, for the first time, closed-loop optimization of impact robustness in real-time control, reducing state discontinuities by up to 51% in simulation while effectively preventing actuator saturation. Experiments on both quadrupedal and humanoid platforms validate the critical role of redundant configurations in enhancing impact robustness.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• impact robustness
• posture optimization
• aerial manipulation
• kinematic redundancy
• robotic impact

Innovation

• impact robustness
• posture optimization
• kinematic redundancy
• task-space inverse dynamics
• min-max optimization