Cooperative Payload Estimation by a Team of Mocobots
Haoxuan Zhang, C. Lin Liu, Matthew Elwin, Randy Freeman, Kevin Lynch
AI summary
Problem
Collaborative manipulation of unknown payloads requires knowledge of inertial properties and grasp locations, which is typically unavailable and hinders safe, high-performance control.
Approach
Robots cooperatively move the payload while synchronously recording twist, acceleration, and wrench data, which a sequential least-squares algorithm processes to estimate grasp kinematics, mass, center of mass, and inertia.
Key results
- Sequential least-squares algorithm for grasp kinematics, mass, CoM, and inertia estimation
- Experimental validation with three Omnid mocobots across four payload configurations
- Accurate property recovery using only joint encoders and series-elastic actuator torques
- Robust estimation without requiring expensive wrist-mounted force-torque sensors
Why it matters
Enables safe, adaptive human-robot collaboration and high-performance autonomous manipulation for unstructured tasks in manufacturing, logistics, and construction.
Abstract
For high-performance autonomous manipulation of a payload by a mobile manipulator team, or for collaborative manipulation with the human, robots should be able to discover where other robots are attached to the payload, as well as the payload’s mass and inertial properties. In this letter, we describe a method for the robots to autonomously discover this information. The robots cooperatively manipulate the payload, and the twist, twist derivative, and wrench data at their grasp frames are used to estimate the transformation matrices between the grasp frames, the location of the payload’s center of mass, and the payload’s inertia matrix.Themethodisvalidatedexperimentallywithateamofthree mobile cobots, or mocobots.