Fast and Safe Trajectory Planning: Solving the Cobot Performance/Safety Trade-Off in Human-Robot Shared Environments

1Research Center "Enrico Piaggio", University of Pisa, 2Munich School of Robotics and Machine Intelligence,Technical University of Munich (TUM)
IEEE Robotics and Automation Letters 2021
Presented at the 2020 IEEE-RAS International Conference on Humanoid Robots

Abstract

The rise of collaborative robotics has offered new opportunities for integrating automation into the factories, allowing robots and humans to work side-by-side. However, this close physical coexistence inevitably brings new constraints for ensuring safe human-robot cooperation. The current paramount challenge is integrating human safety constraints without compromising the robotic performance goals, which require minimization of the task execution time alongside ensuring its accomplishment. This paper proposes a novel robot trajectory planning algorithm to produce minimum-time yet safe motion plans along specified paths in shared workspaces with humans. To this end, a safety module was used to evaluate the safety of a time-optimal trajectory iteratively. A safe replanning module was developed to optimally adapt the generated trajectory online whenever the optimal plan violates dynamically provided safety limits. In order to preserve performance , a recovery trajectory planning algorithm was included such that the robot is allowed to restore higher speed motions as soon as the safety concern has been resolved. The proposed solution's effectiveness was evaluated both in simulations and real experiments with two robotic manipulators.

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BibTeX

@article{palleschi2021fast,
        title={Fast and safe trajectory planning: Solving the cobot performance/safety trade-off in human-robot shared environments},
        author={Palleschi, Alessandro and Hamad, Mazin and Abdolshah, Saeed and Garabini, Manolo and Haddadin, Sami and Pallottino, Lucia},
        journal={IEEE Robotics and Automation Letters},
        volume={6},
        number={3},
        pages={5445--5452},
        year={2021},
        publisher={IEEE}
      }
      

Acknowledgement

This work was supported in part by the European Unions Horizon 2020 research and innovation program as part of the projects ILIAD (Grant no.732737), and in part by the Italian Ministry of Education and Research in the framework of the CrossLab project (Departments of Excellence).