PhD Thesis Defense
Tether management is a key issue for extreme terrain robots like Axel, a tethered cliff-rappelling rover. In this talk I present a novel algorithm for tethered motion planning produced by combining shortest-homotopic-path algorithms from the topology and computational geometry communities with traditional graph search methods.
In the case of a rover on a steep slope, avoiding tether entanglement constrains the robot's ascent and descent paths to the same homotopy class. A motion planner must also ensure that this ascent-descent path pair is feasible by analyzing the taut tether configuration, which is the shortest path in the homotopy class of that path pair. Searching the shortest-path tree for these configurations improves the planning algorithm's efficiency, which I demonstrate on a Martian crater data set such as might be seen for a typical mission.
Frictional tether-terrain interaction may cause dangerously intermittent and unstable tether obstacles, which can be categorized based on their stability. I describe how to modify the map to allow for these intermittent obstacles, and how to alter a motion plan around those that present slip hazards.
Together, these algorithms and methods form a framework for tethered motion planning on extreme terrain.