Deep Neural Networks for Swept Volume Prediction Between Configurations
This work addresses the problem of slow SV computation for motion planning in robotics, offering a significant speedup for applications like collision detection and sampling-based planning, though it is incremental as it applies existing DNN methods to a known bottleneck.
The paper tackles the computational expense of traditional Swept Volume (SV) algorithms in motion planning by training Deep Neural Networks (DNNs) to predict SV size for specific robot geometries, achieving estimations close to true SV size and being over 1500 times faster than a state-of-the-art algorithm.
Swept Volume (SV), the volume displaced by an object when it is moving along a trajectory, is considered a useful metric for motion planning. First, SV has been used to identify collisions along a trajectory, because it directly measures the amount of space required for an object to move. Second, in sampling-based motion planning, SV is an ideal distance metric, because it correlates to the likelihood of success of the expensive local planning step between two sampled configurations. However, in both of these applications, traditional SV algorithms are too computationally expensive for efficient motion planning. In this work, we train Deep Neural Networks (DNNs) to learn the size of SV for specific robot geometries. Results for two robots, a 6 degree of freedom (DOF) rigid body and a 7 DOF fixed-based manipulator, indicate that the network estimations are very close to the true size of SV and is more than 1500 times faster than a state of the art SV estimation algorithm.