Elena Stracca, Alessandro Palleschi, Lucia Pallottino and Paolo Salaris
Proceedings of IEEE 20th International Conference on Automation Science and Engineering (CASE)
Abstract
This paper presents a novel trajectory planning method for collaborative robots, focusing on a risk-centric approach to enhance safety and performance. Drawing from risk management principles, encompassing factors such as operator safety, performance degradation, and internal robotrelated issues, the proposed framework offers a comprehensive
solution for offline trajectory planning and online reactive trajectory execution. The main contributions include developing and implementing a risk definition and assessment framework utilizing fuzzy inference systems, a mapping strategy leveraging fuzzy logic to correlate robot states with risk levels, and formulating a global measure for evaluating the overall risk. Additionally, the paper introduces a risk-driven motion planning algorithm aimed at minimizing trajectory risk. It also proposes a reactive trajectory adaptation method to respond dynamically to elevated risk levels during task execution. Validation through simulations and experiments with a 7 Degree of Freedom (DoF) robotic manipulator demonstrates the effectiveness of the proposed approach in generating risk-limited trajectories and adapting online to collision risk factors.
@INPROCEEDINGS{10711449,
author={Stracca, Elena and Palleschi, Alessandro and Pallottino, Lucia and Salaris, Paolo},
booktitle={2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
title={Plan it Safe: A Risk-Driven Motion Planning Framework for Collaborative Robots},
year={2024},
volume={},
number={},
pages={2885-2892},
keywords={Fuzzy logic;Measurement;Trajectory planning;Collaborative robots;Trajectory;Planning;Safety;Risk management;Monitoring;Manipulator dynamics},
doi={10.1109/CASE59546.2024.10711449}}