| || || Robots -- Motion -- Mathematical models|
| || || Governing robotic motion via a single-layer artificial neural network |
Author: Prasad, Avinesh
Institution: University of the South Pacific.
Subject: Robots -- Motion -- Mathematical models , Neural networks (Computer science)
Call No.: Pac TJ 211 .4 .P73 2012
Copyright:10-20% of this thesis may be copied without the authors written permission
Abstract: This thesis addresses the design and implementationof a new approach to address the motion planning and control problem of two-dimensional and three-dimensional robots. The approach of solving the problem is decoupled. Firstly, there is an establishment of a uniquely tailored velocity algorithm which is capable of driving the robot from its initial position to the target positionand remain there forever. Secondly, a supervised single-layer artificial neural network, which employs an arctan activation function, is used to model the turning/steering angle of the robot ensuring that the robot steers safely pass an obstacle. A simple and easy method is also developed for obtaining a set of data for training the network. The training data is obtained using computer simulationwhere the initial path is traced by the user. With data obtained, the neural network is then trained using the least square method. In this thesis, the purpose of the single-layer artificial neural network is to control the motion of the robot when the robot is in the sensing zone of the obstacle. Otherwise, the velocity algorithm is enough to drive the robot to its target in the absence of obstacles. Various kind of obstacles such as fixed, moving and artificial obstacles are studied within the collision avoidance scheme. It has been noticed that our method is efficiently useful in designing control laws that can incorporate these obstacles. The mechanical singularities of the robot are carefully taken into account either by treating it as an artificial obstacle or by incorporating it into the control laws. Moreover, the stability of the system is also studied via the Direct Method of Lyapunov. The work carried out naturally falls into three distinctive parts.