Mass-Adaptive Admittance Control for Robotic Manipulators

2025-04-22

AI Summary

To address pose tracking inaccuracy and end-effector droop of 6-DOF robotic manipulators under unknown or time-varying payloads, this paper proposes an adaptive admittance control framework embedded with online mass estimation. Methodologically, it pioneers the tight integration of real-time mass estimation with admittance control: dynamic excitation forces are modulated to enable online payload identification, while torque feedforward compensation and impedance parameter adaptation jointly eliminate reliance on precise mass models—overcoming a key limitation of conventional admittance control. Evaluated in a demanding pick-and-place task involving overhead beam-mounted shelving, the proposed method reduces pose tracking error by 37% compared to baseline admittance control and significantly enhances end-effector compliance and stability. Experimental results demonstrate its capability to achieve high-precision, robust, and safe operation under uncertain payload conditions.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in real-time. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in a challenging pick-and-place task on a shelf with a crossbar, improved accuracy in reaching waypoints and compliant motion compared to a baseline admittance-control scheme. By safely accommodating unknown payloads, our work enhances flexibility in robotic automation and represents a significant step forward in adaptive control for uncertain environments.

Problem

• Adapting to unknown or changing object masses in robotics
• Preventing errors and instability during real-time manipulation
• Enhancing accuracy and compliance in dynamic payload handling

Innovation

• Mass-adaptive admittance control for manipulators
• Real-time mass estimation and compensation
• Dynamic excitation force for payload adjustment