  This paper presents reactive obstacle and self-collision avoidance of
redundant robotic manipulators within real time kinematic feedback control
using GPU-computed distance transform. The proposed framework utilizes
discretized representation of the robot and the environment to calculate 3D
Euclidean distance transform for task-priority based kinematic control. The
environment scene is represented using a 3D GPU-voxel map created and updated
from a live pointcloud data while the robotic link model is converted into a
voxels offline and inserted into the voxel map according to the joint state of
the robot to form the self-obstacle map. The proposed approach is evaluated
using the Tiago robot, showing that all obstacle and self collision avoidance
constraints are respected within one framework even with fast moving obstacles
while the robot performs end-effector pose tracking in real time. A comparison
of related works that depend on GPU and CPU computed distance fields is also
presented to highlight the time performance as well as accuracy of the GPU
distance field.
