Motion planning of a pneumatic robot using a neural network

Abstract

Integration of sensing and motion planning plays a crucial role in autonomous robot operation. We present a framework for sensor-based robot motion planning that uses learning to handle arbitrarily configured sensors and robots. The theoretical basis of this approach is the concept of the perceptual control manifold that extends the notion of the robot configuration space to include sensor space. To overcome modeling uncertainty, the topology-representing-network algorithm is employed to learn a representation of the perceptual control manifold. By exploiting the topology-preserving features of the neural network, a diffusion-based path planning strategy leads to flexible obstacle avoidance. The practical feasibility of the approach is demonstrated on a pneumatically driven robot arm (SoftArm) using visual sensing.