Path Planning for Autonomous Ground Vehicles using GNSS and Cellular LTE Signal Reliability Maps and GIS 3-D Maps
- Author(s): Ragothaman, Sonya
- Advisor(s): Kassas, Zak M
- et al.
In this thesis, path planning for an autonomous ground vehicle (AGV) in an urban environment is considered. The following problem is considered. starting from an initial location, the AGV desires to reach a final location by taking the shortest distance, while minimizing the AGVs position estimation error and guaranteeing that the AGVs position estimation uncertainty is below a desired threshold. The AGV is assumed to be equipped with receivers capable of producing pseudodange measurements on Global Navigation Satellite System (GNSS) satellites and cellular long-term evolution (LTE) towers. Using a geographic information system (GIS) three-dimensional (3-D) building map of the urban environment, a signal reliability map is introduced, which provides information about regions where large errors due to cellular signal multipath or poor GNSS line-of-sight (LOS) are expected. The vehicle uses the signal reliability map to calculate the position estimation mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived for a weighted nonlinear least-squares (WNLS) estimator, which is used to find an analytical upper bound on the position bias due to multipath. A path planning approach based on Dijkstra's algorithm is proposed to optimize the AGV's path while minimizing the path length and the position estimation MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath being below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths. Simulation results are presented demonstrating that utilizing ambient cellular LTE signals together with GNSS signals (1) reduces the uncertainty about the AGV's position, (2) increases the number of feasible paths to choose from, which could be useful if other considerations arise, e.g., traffic jams and road blockages due to construction, and (3) yields significantly shorter feasible paths, which would otherwise be infeasible with GNSS signals alone. Experimental results on a ground vehicle navigating in downtown Riverside, California, are presented demonstrating a close match between the simulated and experimental results.