An Artificial Neural Network Based Learning Method for Mobile Robot Localization

One of the most used artificial neural networks (ANNs) models is the well-known MultiLayer Perceptron (MLP) [Haykin, 1998]. The training process of MLPs for pattern classification problems consists of two tasks, the first one is the selection of an appropriate architecture for the problem, and the second is the adjustment of the connection weights of the network. Extensive research work has been conducted to attack this issue. Global search techniques, with the ability to broaden the search space in the attempt to avoid local minima, has been used for connection weights adjustment or architecture optimization of MLPs, such as evolutionary algorithms (EA) [Eiben & Smith, 2003], simulated annealing (SA) [Jurjoatrucj et al., 1983], tabu search (TS) [Glover, 1986], ant colony optimization (ACO) [Dorigo et al., 1996] and particle swarm optimization (PSO) [Kennedy & Eberhart, 1995]. The NeuroEvolution of Augmenting Topologies (NEAT) [Stanley & Miikkulainen, 2002] method turns the neural networks topology and connect weight simultaneioulsy using an evolutionary computation method. It evolves efficient ANN solutions quickly by complexifying and optimizing simultaneously; it achieves performance that is superior to comparable fixed-topology methods. In [Patan & Parisini, 2002] the stochastic methods Adaptive Random Search (ARS) and Simultaneous Perturbation Stochastic Approximation (SPSA) outperformed extended dynamic backpropagation at training a dynamic neural network to control a sugar factory actuator. Recently, artifical neural networks based methods are applied to robotic systems. In [Racz & Dubrawski, 1994], an ANN was trained to estimate a robot’s position relative to a particular local object. Robot localization was achieved by using entropy nets to implement a regression tree as an ANN in [Sethi & Yu, 1990]. An ANN was trained in [Choi & Oh, 2007] to correct the pose estimates from odometry using ultrasonic sensors. In this paper, we propose an aritifucal neural networks learning method for mobile robot localization, which combines the two popular swarm inspired methods in computational intelligence areas: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to train the ANN models. ACO was inspired by the behaviors of ants and has many successful applications in discrete optimization problems. The particle swarm concept originated as a simulation of a simplified social system. It was found that the particle swarm model could be used as an optimizer. These algorithms have been applied already to solving O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m