In: Advanced Robotics, Vol. 0, Pages 1-17, Taylor & Francis Online, 2020.
Abstract
In this paper we present a software-based approach for collision avoidance that can be applied in human-robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance.
@article{pub10772,
author = {
Mronga, Dennis
and
Knobloch, Tobias
and
de Gea Fernández, José
and
Kirchner, Frank
},
title = {A Constraint-Based Approach for Human-Robot Collision Avoidance},
year = {2020},
pages = {1--17},
journal = {Advanced Robotics},
publisher = {Taylor & Francis Online}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz