Fast deep swept volume estimator
Abstract
Despite decades of research on efficient swept volume computation for robotics, computing the exact swept volume is intractable and approximate swept volume algorithms have been computationally prohibitive for applications such as motion and task planning. In this work, we employ deep neural networks (DNNs) for fast swept volume estimation. Since swept volume is a property of robot kinematics, a DNN can be trained off-line once in a supervised manner and deployed in any environment. The trained DNN is fast during on-line swept volume geometry or size inferences. Results show that DNNs can accurately and rapidly estimate swept volumes caused by rotational, translational, and prismatic joint motions. Sampling-based planners using the learned distance are up to five times more efficient and identify paths with smaller swept volumes on simulated and physical robots. Results also show that swept volume geometry estimation with a DNN is over 98.9% accurate and 1,200 times faster than an octree-based swept volume algorithm.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 02, 2020
- Source ID
- 10.1177/0278364920940781
Entities
People
- Aleksandra Faust
- Hao-Tien Lewis Chiang
- John Eg Baxter
- Lydia Tapia
- Mohammad R. Yousefi
- Satomi Sugaya
Organizations
- Air Force Research Laboratory
- National Science Foundation
- University of New Mexico