Optimized Grid Voxelization for Obstacle Avoidance in Collaborative Robotics

Voxelization into a fixed voxel grid is used for simplicity of processing and data format for applications such as dynamic re-planing of a collaborative robot path around a dynamic obstacle. In this case, it may be undesirable that conventional voxelization methods generate more volume, which results in a longer generated path than needed if the real shape of the obstacle was used instead of voxels. The approach proposed in this paper utilizes triangular normals of the mesh formed by neighboring points in a depth image to adjust the occupancy of a fixed voxel grid, ensuring the voxels more accurately represent the true volume of the scanned object. An experiment with real hardware was performed to compare the proposed voxelization method with the conventional fixed grid voxelization method. The collected point clouds are voxelized using both methods, and the resulting voxel maps are then passed to a simple path solver to find the length of the robot path around the obstacle. The results indicate that our proposed method achieves better results in terms of shorter avoidance paths, as our method provides a tighter representation of the obstacle. The average increase in the length of the alternative avoidance path compared to the theoretical shortest path around the object was 15.7 % for the conventional method and 8.6 % for the presented method. Overall, the experimental results indicate the usefulness of the developed system, as it can shorten the robot cycle time when avoiding obstacles.