A Grid-Based Path Planning Approach for a Two Vehicle Team with Localization Constraints
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This research proposes a path-finding method for two unmanned vehicles with localization constraints using a modified shortest-path algorithm. A beacon vehicle has GPS or other absolute positioning information, and a target vehicle has only bearing information taken relative to the beacon vehicle or known stationary landmarks. A method for calculating edge costs is described based on factoring the covariance associated with an Extended Kalman filter. By gridding the region and discretizing the position error, the path-planning problem for two vehicles can be formulated in state space and solved using a dynamic programming algorithm. To improve the computation time of the algorithm, a heuristic is also introduced. In simulation, paths found from the dynamic programming method and heuristic consistently outperform a greedy algorithm and find paths that favor localizable regions and result in relatively low amounts of error.
Garber, Mark (2017). A Grid-Based Path Planning Approach for a Two Vehicle Team with Localization Constraints. Master's thesis, Texas A & M University. Available electronically from