A decision-theoretic formalism for belief-optimal reasoning
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Bounding on rough terrain with the LittleDog robot
International Journal of Robotics Research
On soft predicates in subdivision motion planning
Proceedings of the twenty-ninth annual symposium on Computational geometry
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Legged vehicles have attracted interest for many high-mobility applications, such as military troop support and logistics in rocky, steep, and forested terrain, scientific exploration of cliffs, mountains, and volcanoes on earth and other planets, and search and rescue. Humanoid robots have additional applications in homes and offices as personal assistants. But planning and controlling their motion is difficult, because dozens of joints must be coordinated to maintain balance while executing a task. It is particularly difficult in extremely uneven, steep, or cluttered terrain—precisely where legs are an advantage. I present a legged locomotion planner that reasons with the physical and operational constraints in the vehicle's high dimensional configuration space, producing motions that are guaranteed to avoid collision and remain balanced. This planner works on a variety of terrain, ranging from flat ground to steep and rocky cliff faces, and is applied to three very different robots: NASA's six-legged lunar vehicle ATHLETE, AIST Japan's biped humanoid robot HRP-2, and Stanford's four-limbed rock climbing robot Capuchin. The planner consists of two parts: first, a graph search to find a sequence of contacts to make and break, and second, the planning of single-step motions between successive contacts using a probabilistic roadmap planner (PRM). I prove that a "fuzzy search", where PRM planning is interleaved between candidate steps, has theoretical completeness properties, and a running time not strongly affected by configuration space dimension. The planner can also produce natural-looking motions given a small number of "motion primitives", high-quality example motions that bias the search toward similar paths. These techniques can be applied to other robot systems that make and break contact, and I present a manipulation planner that enables the Honda ASIMO humanoid robot to push objects on a table, in simulation and on the real robot.