Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold
Steven Jens Jorgensen, Mihir Vedantam, Ryan Gupta, Henry Cappel, Luis Sentis
Abstract
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 an injective 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. Numerical simulations are performed with the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios.
BibTeX
@inproceedings{icra2020_findinglocomanip,
title = {Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold},
author = {Steven Jens Jorgensen and Mihir Vedantam and Ryan Gupta and Henry Cappel and Luis Sentis},
booktitle = {ICRA 2020},
year = {2020}
}