Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold
This addresses the challenge of efficient whole-body kinodynamic planning for humanoid robots performing locomanipulation tasks, representing an incremental improvement in robotic motion planning.
The paper tackles the problem of generating simultaneous locomotion and manipulation plans for robots by formulating it as a weighted-A* graph search on a locomotion constraint manifold, and demonstrates the method on the NASA Valkyrie robot with two example scenarios.
We present a method that finds locomanipulation plans that perform simultaneous locomotion and manipulation of objects for a desired end-effector trajectory. Key to our approach is to consider a generic locomotion constraint manifold that defines the locomotion scheme of the robot and then using this constraint manifold to search for admissible manipulation trajectories. The problem is formulated as a weighted-A* graph search whose planner output is a sequence of contact transitions and a path progression trajectory to construct the whole-body kinodynamic locomanipulation plan. We also provide a method for computing, visualizing and learning the locomanipulability region, which is used to efficiently evaluate the edge transition feasibility during the graph search. Experiments are performed on the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios.