Real-Time Planning with Primitives for Dynamic Walking over Uneven Terrain
This addresses the problem of efficient and fast motion planning for robotic walkers on challenging terrain, though it appears incremental as it builds on existing methods like virtual constraints and greedy search.
The paper tackles real-time motion planning for underactuated dynamic walking on uneven terrain by using motion primitives defined as virtual holonomic constraints, resulting in an algorithm that can plan several footsteps ahead in a fraction of a second for two walker models.
We present an algorithm for receding-horizon motion planning using a finite family of motion primitives for underactuated dynamic walking over uneven terrain. The motion primitives are defined as virtual holonomic constraints, and the special structure of underactuated mechanical systems operating subject to virtual constraints is used to construct closed-form solutions and a special binary search tree that dramatically speed up motion planning. We propose a greedy depth-first search and discuss improvement using energy-based heuristics. The resulting algorithm can plan several footsteps ahead in a fraction of a second for both the compass-gait walker and a planar 7-Degree-of-freedom/five-link walker.