Impact-Aware Task-Space Quadratic-Programming Control (2006.01987v6)

Abstract: Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot's linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers' stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically-computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

• The paper presents an impact-aware QP control framework that anticipates collision dynamics to prevent hardware damage.
• It employs a 3D impact model integrating Coulomb’s friction cone and momentum conservation to define feasible impulse sets.
• Experimental tests on the Panda manipulator and HRP-4 robot confirm enhanced efficiency and robustness in managing high-speed impacts.

Impact-Aware Task-Space Quadratic-Programming Control

This paper presents a framework for improving the control of robots during purposeful impact tasks, leveraging a task-space quadratic programming (QP) approach formulated to handle the dynamic and potential destructive forces during collisions. The research addresses the limitations of existing controllers that typically rely on smooth dynamics assumptions, rendering them inappropriate for managing the abrupt changes induced by impact events.

Research Context and Objectives

Robots often need to make contact with their environment at high velocities, such as in tasks involving walking, jumping, or manipulation activities. Such operations result in impulsive forces that can propagate through the robot's structure, potentially causing mechanical damage. Traditional mitigative strategies involve minimizing contact speeds, yet they do not accommodate functionalities requiring high-speed interactions. This paper proposes a novel control strategy that anticipates and manages impact-induced velocity fluctuations to maintain stability and adhere to hardware constraints, thereby minimizing damage risks and improving task performance.

Methodological Advances

1. Impact-Aware Control Framework: The research introduces a control architecture enhanced with impact-aware constraints. These constraints modify the search space within the QP formulation, ensuring that post-impact states remain within hardware resilience limits. This method not only prepares the controller for the expected impact event but also optimizes for the highest feasible pre-impact speeds.
2. Taking Impact Dynamics into Account: The 3D impact model integrates Coulomb’s friction cone principles and leverages task-space momentum conservation during impact. Through the analytical construction of feasible impulse sets, represented as polyhedra, the model provides a rigorous framework for anticipating and counteracting state jumps across different robot components, including joint velocities and joint torques.
3. Application of the Theory: The framework has been tested with the Panda manipulator and HRP-4 humanoid robot to manage impacts effectively, demonstrating high contact velocities with minimized risk of damage. These practical applications highlighted the robustness of the proposed model in real-world scenarios, particularly in tasks demanding rapid interaction with the environment, such as swift object grasping and manipulation.

Results and Findings

• Experimental Validation:

The impact-aware QP controller succeeded in regulating joint torques and velocities within the identified polyhedral constraints during impact scenarios, confirming the accuracy of the model's predictions.

• Increased Task Efficiency:

By accommodating intentional impacts through advanced constraints, robots could execute tasks involving high-speed contact more efficiently without compromising safety or robot integrity.

• Robustness and Adaptation:

The framework demonstrated resilience to rapid adaptation in varying operational contexts, suggesting its applicability across a spectrum of robotic tasks requiring precise and high-speed interactions.

Conclusions and Future Directions

The paper provides a pivotal step toward integrating robust impact-aware control mechanisms in robot systems, enabling them to perform high-speed tasks safely. Future research could explore extending these methods to systems experiencing multiple or asynchronous impacts, refining sensitivity analyses to improve the precision of impact predictions. Enhancements in model constraints could further optimize the QP controller's adaptability in environments exhibiting diverse dynamic challenges.

In summary, this research effectively bridges the gap between theoretical impact dynamics and practical control architectures in robotics, contributing significantly to the field of robust, impact-resilient robotic systems.