Affiliation:
1. University of New Mexico, Albuquerque, NM, USA
2. Robotics at Google, Mountain View, CA, USA
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.
Funder
National Science Foundation
Air Force Research Laboratory
Subject
Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Collision-Free Volume Estimation Algorithm for Robot Motion Deformation;2023 21st International Conference on Advanced Robotics (ICAR);2023-12-05
2. Fast Collision Detection for Robot Manipulator Path: an Approach Based on Implicit Neural Representation of Multiple Swept Volumes;2023 International Conference on Advanced Robotics and Intelligent Systems (ARIS);2023-08-30
3. Multitask and Transfer Learning of Geometric Robot Motion;2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021-09-27
4. Deep Prediction of Swept Volume Geometries: Robots and Resolutions;2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2020-10-24