Efficient Probabilistic Collision Detection for Non-Convex Shapes
This addresses collision detection challenges in robotics and simulation, but appears incremental as it builds on hierarchical representations for non-convex models.
The paper tackled the problem of fast probabilistic collision detection for both convex and non-convex shapes, achieving efficient performance on synthetic benchmarks and a 7-DOF robot arm trajectory planning task.
We present new algorithms to perform fast probabilistic collision queries between convex as well as non-convex objects. Our approach is applicable to general shapes, where one or more objects are represented using Gaussian probability distributions. We present a fast new algorithm for a pair of convex objects, and extend the approach to non-convex models using hierarchical representations. We highlight the performance of our algorithms with various convex and non-convex shapes on complex synthetic benchmarks and trajectory planning benchmarks for a 7-DOF Fetch robot arm.