Stefan Ulbrich

Reactive Neural Path Planning with Dynamic Obstacle Avoidance in a Condensed Configuration Space

We present a biologically inspired approach for path planning with dynamic obstacle avoidance. Path plan-ning 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 C