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

Traditional approaches to motion modeling for skid-steer robots struggle to capture 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 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. The GPU implementation of the proposed method and supplementary video footage are available at https://stochasticmppi.github.io.

Authors

• Ananya Trivedi
• Sarvesh Prajapati
• Anway Shirgaonkar
• Mark Zolotas
• Taskin Padir

Keywords

• Adaptation models
• Navigation
• Dynamics
• Graphics processing units
• Kinematics
• Real-time systems
• Hardware
• Collision avoidance
• Visual perception
• Videos
• Robot Navigation
• Stochastic Model Predictive Control
• Optimization Problem
• Kinematic
• Optimal Control
• Nonlinear Dynamics
• Gaussian Process
• Kriging
• Motion Model
• Optimal Control Problem
• Obstacle Avoidance
• Stochastic Problem
• Stochastic Control
• Hardware Experiments
• Neural Network
• Collision
• Cost Function
• Simulation Experiments
• Control Input
• Angular Velocity
• Gaussian Process Model
• Linear Velocity
• Path Tracking
• Signed Distance Function
• Path Planning
• Nominal Model
• Sharp Turn
• Safety Constraints
• Convex Optimization Problem
• Dense Sand