  We present a biologically inspired approach for path planning with dynamic
obstacle avoidance. Path planning is performed in a condensed configuration
space of a robot generated by self-organizing neural networks (SONN). The robot
itself and static as well as dynamic obstacles are mapped from the Cartesian
task space into the configuration space by precomputed kinematics. The
condensed space represents a cognitive map of the environment, which is
inspired by place cells and the concept of cognitive maps in mammalian brains.
The generation of training data as well as the evaluation were performed on a
real industrial robot accompanied by simulations. To evaluate the reactive
collision-free online planning within a changing environment, a demonstrator
was realized. Then, a comparative study regarding sample-based planners was
carried out. So we could show that the robot is able to operate in dynamically
changing environments and re-plan its motion trajectories within impressing
0.02 seconds, which proofs the real-time capability of our concept.
