CC-VPSTO: Chance-Constrained Via-Point-based Stochastic Trajectory Optimisation for Safe and Efficient Online Robot Motion Planning
<|reference_start|>CC-VPSTO: Chance-Constrained Via-Point-based Stochastic Trajectory Optimisation for Safe and Efficient Online Robot Motion Planning: Safety in the face of uncertainty is a key challenge in robotics. We introduce a real-time capable framework to generate safe and task-efficient robot motions for stochastic control problems. We frame this as a chance-constrained optimisation problem constraining the probability of the controlled system to violate a safety constraint to be below a set threshold. To estimate this probability we propose a Monte--Carlo approximation. We suggest several ways to construct the problem given a fixed number of uncertainty samples, such that it is a reliable over-approximation of the original problem, i.e. any solution to the sample-based problem adheres to the original chance-constraint with high confidence. To solve the resulting problem, we integrate it into our motion planner VP-STO and name the enhanced framework Chance-Constrained (CC)-VPSTO. The strengths of our approach lie in i) its generality, without assumptions on the underlying uncertainty distribution, system dynamics, cost function, or the form of inequality constraints; and ii) its applicability to MPC-settings. We demonstrate the validity and efficiency of our approach on both simulation and real-world robot experiments.<|reference_end|>
@article{brudermüller2024cc-vpsto:,
    title={CC-VPSTO: Chance-Constrained Via-Point-based Stochastic Trajectory
  Optimisation for Safe and Efficient Online Robot Motion Planning},
    author={Lara Bruderm"uller, Guillaume Berger, Julius Jankowski, Raunak
  Bhattacharyya, Rapha"el Jungers, Nick Hawes},
    journal={arXiv preprint arXiv:2402.01370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01370},
    primaryClass={cs.RO cs.SY eess.SY}
}