A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios
This work addresses the problem of safe and efficient autonomous navigation in challenging off-road environments for applications like military or agricultural vehicles, but it appears incremental as it combines existing methods like dynamic programming and feedback linearization.
The paper tackles autonomous off-road driving by developing a data-driven motion planning and control system that integrates GIS and environmental data for terrain traversability, with simulation results demonstrated in Oregon and Indiana case studies.
This paper presents a novel data-driven approach to vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desired trajectory between waypoints such that infeasible control states and collisions with obstacles are avoided. The LPP also updates the GRP with real-time sensing and control data. A low-level feedback controller applies feedback linearization to asymptotically track the specified LPP trajectory. Autonomous driving simulation results are presented for traversal of terrains in Oregon and Indiana case studies.