Date of Award
Master of Science in Electrical Engineering (MSEE)
Electrical, Computer, and Biomedical Engineering
A path planning method using Model Predictive Control (MPC) is explored for driving the state of the robot towards a goal while utilizing knowledge of which states are fully observable to simultaneously minimize state uncertainty. State estimation is accomplished using a particle filter. Path planning is accomplished using multiple particle filters to track the performance of the hypothetical state estimates while propagated out to a specified time horizon. Paths are continuously evaluated using a scoring function, yielding a final "best path" selection that is implemented by the robot controller to achieve the best blend of performance - seeking the goal while simultaneously minimizing state estimate uncertainty. Crucially, hypothetical paths are generated using breadth-first search of the state space and not intentionally driven towards observable states. The proposed path planner was tested in simulation and gave superior results over a naive navigation scheme in many evaluated scenarios of sparse environment observability.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Altobelli, Kent H., "PATH PLANNING FOR ROBOT NAVIGATION IN SPARSELY OBSERVABLE ENVIRONMENTS" (2023). Open Access Master's Theses. Paper 2305.