Real-Time Mass Estimation with a Robot Arm for Advanced Manufacturing
College
College of Engineering & Technology (Batten)
Department
MAE
Graduate Level
Doctoral
Graduate Program/Concentration
Mechanical and Aerospace Engineering
Presentation Type
Poster Presentation
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.
We experimentally demonstrated on a UR5e robot arm that we can accurately estimate the unknown mass of the payload, which enabled us to achieve motion that is both compliant and accurate. This expresses the feasibility of our approach for uncertain environments like dynamic pick-and-place operations where accurate payload information is required for a more stable and efficient manipulation.
Keywords
Adaptive control, Admittance control, Mass estimation, Trajectory planning, Real-time control, Constrained motion planning, Robotic manipulators, Smart manufacturing
Real-Time Mass Estimation with a Robot Arm for Advanced Manufacturing
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.
We experimentally demonstrated on a UR5e robot arm that we can accurately estimate the unknown mass of the payload, which enabled us to achieve motion that is both compliant and accurate. This expresses the feasibility of our approach for uncertain environments like dynamic pick-and-place operations where accurate payload information is required for a more stable and efficient manipulation.