Predicting Sample Collision with Neural Networks
This addresses the problem of expensive collision detection in high-dimensional robotics motion planning, offering an incremental improvement over existing methods.
The paper tackled the high computational cost of collision detection in motion planning for robotics by developing a framework that uses a Contractive AutoEncoder and a Multilayer Perceptron to predict sample collision states efficiently, showing computational efficiency and good generalization across various 2D and 3D planning problems.
Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures a occupancy grids representation of the robot's workspace, and a Multilayer Perceptron, which efficiently predicts the collision state of the robot from the CAE and the robot's configuration. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.