VISION-BASED ROBOT MOTION PLANNING USING A TOPOLOGY REPRESENTING NEURAL NETWORK

Abstract

The goal of integrating sensors into robot motion planning has incited recent re-search eorts. The Perceptual Control Manifold serves this goal extending the notion of the robot conguration space to include sensor space. In this paper, we develop a framework for sensor-based motion planning of robotic manipulators using the Topology Representing Network algorithm to develop a learned repre-sentation of the Perceptual Control Manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diusion-based path planning strategy for flexible obstacle avoidance. We demonstrate the ca-pabilities of topology preserving maps using an industrial robot simulator and a pneumatically driven robot arm (SoftArm). 1