A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles
Abstract
A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 13, 2018
- Source ID
- 10.1115/1.4038905
Entities
People
- Angshuman Goswami
- Ardalan Vahidi
- Chen Zhang
- Judhajit Roy
- Nianfeng Wan
- Paramsothy Jayakumar
Organizations
- Clemson University
- Ford Motor Company
- United States Army Tank Automotive Research, Development and Engineering Center