Collision detection, isolation and identification

Implemented on a legged manipulator

Master thesis (2022)

Authors

J.I. van Dam Mechanical Engineering

Contributors

J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
David Abbink Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (mentor)
Andreea Tulbure (coach)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2022 Jessie van Dam
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Published Date

01-03-2022

Language

English

Reuse Rights

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Faculty

Mechanical Engineering

Abstract

To safely deploy legged robots in the real world, it is necessary to provide them with the ability to reliably detect unexpected contacts and accurately estimate the corresponding contact force. Therefore, a collision detection, isolation, and identification pipeline is proposed for a quadrupedal manipulator. An approach based on band-pass filtered forces is presented which accurately detects a collision, and estimates the collision time span. Next, the colliding body link is isolated. Finally, a collision identification method accurately identifies the magnitude and direction of the force. It is robust against model inaccuracies, unmodeled loads and any other potential source of disturbances acting on the robot. The framework is validated using extensive hardware experiments in various scenarios summing up to 416 collisions, including trotting and additional unmodeled load on the robot.

Files

Final_report_jessie_van_dam.pd... (.pdf)
(.pdf | 11.8 Mb)
- Embargo expired in 01-03-2023