Data-Driven Sampling-Based Stochastic MPC for Skid-Steer Mobile Robot Navigation

Ananya Trivedi1, Sarvesh Prajapati1, Anway Shirgaonkar1, Mark Zolotas1, and Taskin Padir1
1Northeastern University, Boston
Paper arXiv Video Code

Abstract

Traditional approaches to motion modeling for skid-steer robots struggle with capturing nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we tackle such nonlinearities by enhancing a dynamic unicycle model with Gaussian Process (GP) regression outputs. This enables us to develop an adaptive, uncertainty-informed navigation formulation. We solve the resultant stochastic optimal control problem using a chance-constrained Model Predictive Path Integral (MPPI) control method. This approach formulates both obstacle avoidance and path-following as chance constraints, accounting for residual uncertainties from the GP to ensure safety and reliability in control. Leveraging GPU acceleration, we efficiently manage the non-convex nature of the problem, ensuring real-time performance. Our approach unifies path-following and obstacle avoidance across different terrains, unlike prior works which typically focus on one or the other. We compare our GP-MPPI method against unicycle and data-driven kinematic models within the MPPI framework. In simulations, our approach shows superior tracking accuracy and obstacle avoidance. We further validate our approach through hardware experiments on a skid-steer robot platform, demonstrating its effectiveness in high-speed navigation.

Video


BibTeX


      @article{trivedi2024datadrivensamplingbasedstochastic,
        title={Data-Driven Sampling Based Stochastic MPC for Skid-Steer Mobile Robot Navigation}, 
        author={Ananya Trivedi and Sarvesh Prajapati and Anway Shirgaonkar and Mark Zolotas and Taskin Padir},
        year={2024},
        eprint={2411.03289},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2411.03289}, 
      }

      @inproceedings{trivedi2024probabilistic,
        title={A probabilistic motion model for skid-steer wheeled mobile robot navigation on off-road terrains},
        author={Trivedi, Ananya and Zolotas, Mark and Abbas, Adeeb and Prajapati, Sarvesh and Bazzi, Salah and Pad{\i}r, Ta{\c{s}}kin},
        booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
        pages={12599--12605},
        year={2024},
        organization={IEEE}
      }
      
      @inproceedings{trivedi2023probabilistic,
        title={Probabilistic Dynamic Modeling and Control for Skid-Steered Mobile Robots in Off-Road Environments},
        author={Trivedi, Ananya and Bazzi, Salah and Zolotas, Mark and Pad{\i}r, Ta{\c{s}}k{\i}n},
        booktitle={2023 IEEE International Conference on Assured Autonomy (ICAA)},
        pages={57--60},
        year={2023},
        organization={IEEE}
      }