查看文章

Swept volume, the volume displaced by a moving object, is an ideal distance metric for sampling-based motion planning because it directly correlates to the amount of motion between two configurations. However, even approximate algorithms are computationally prohibitive. Our fundamental approach is the application of deep learning to efficiently estimate swept volume computation within a 5%–10% error for all robots tested, from rigid bodies to manipulators. However, even inference via the trained network can be computationally costly given the often hundreds of thousands of computations required by sampling-based motion planning. To address this, we demonstrate an efficient hierarchal approach for applying our trained estimator. This approach first pre-filters samples using a weighted Euclidean estimator trained via swept volume. Then, it selectively applies the deep neural network estimator. The first …