Learning the perceptual control manifold for sensor-based robot path planning

The perceptual control manifold is a concept that extends the notion of the robot configuration space to include sensor feedback for robot motion planning. In this paper, we propose a framework for sensor-based robot motion planning using the topology representing network algorithm to develop a learned representation of the perceptual control manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diffusion-based path planning strategy for flexible obstacle avoidance. Simulations on path control and flexible obstacle avoidance demonstrate the feasibility of this approach for motion planning and illustrate the potential for further robotic applications