  With advancing technologies, robotic manipulators and visual environment
sensors are becoming cheaper and more widespread. However, robot control can be
still a limiting factor for better adaptation of these technologies. Robotic
manipulators are performing very well in structured workspaces, but do not
adapt well to unexpected changes, like people entering the workspace. We
present a method combining 3D Camera based workspace mapping, and a predictive
and reflexive robot manipulator trajectory estimation to allow more efficient
and safer operation in dynamic workspaces. In experiments on a real UR5 robot
our method has proven to provide shorter and smoother trajectories compared to
a reactive trajectory planner in the same conditions. Furthermore, the robot
has successfully avoided any contact by initialising the reflexive movement
even when an obstacle got unexpectedly close to the robot. The main goal of our
work is to make the operation more flexible in unstructured dynamic workspaces
and not just avoid obstacles, but also adapt when performing collaborative
tasks with humans in the near future.
