Date of Award
Department of Computer Science
This thesis explores two novel approaches to sample-based motion planning that utilize regressions as continuous function approximations to reduce the memory cost of planning. The first approach, Field Search Trees (FST) provides a solution for single-start planning by iteratively building local regressions of the cost-to-arrive function. The second approach, the Regression Complex (RC), constructs a complex of cells with each cell containing a regression of the distance between any two points on its boundary, creating a useful data structure for any start and goal planning query. We provide formal definitions of both approaches and experimental results of running the algorithms on different simulated robot systems. We conclude that regression-based motion planning provides key advantages over traditional sample-based motion planning in certain cases, but more work is required to extend these approaches into higher dimensional configuration spaces.
Putman, Josiah K., "Regression-based motion planning" (2020). Dartmouth College Undergraduate Theses. 150.