Vision‐based path following by using a neural network Guidance System

Abstract

This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view‐angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high‐contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single‐speed implementation and limited network training. Phase II featured a multi‐speed implementation using adaptively varied view‐angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi‐speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.

Structure Adaptable Digital Neural Network Techniques in Visual Servoing of Robotic Manipulators

“…Van der Smagt et al [16] demonstrated visual servoing of a manipulator using a MLP capable of on-line learning using two computer workstations running in parallel. Other MLP based approaches include [17], [18], [19] and [20].…”