Topology Representing Network for Sensor-Based Robot Motion Planning

We present a framework for sensor-based motion planning of robotic manipulators using a topology representing network (TRN). Exploiting the perfectly topology preserving features of the network, the algorithm learns the representation of the Perceptual Control Manifold (PCM), a recently introduced concept for motion planning. This concept allows sensors to be integrated into robot motion planning. Besides a demonstration of the technical feasibility of motion planning through perfectly topology preserving maps the capabilities of this approach within an engineering framework, namely the implementation on a pneumatically driven robot arm (SoftArm), are demonstrated.