Path Planning in RoboCup Soccer Simulation 3D Using Evolutionary Artificial Neural Network
RoboCup Soccer offers a challenging platform for intelligent soccer agents to continuously perceive their environment and make smart decisions autonomously.During a soccer match, once a robot takes possession of the bali, the most important decision it has to make is to plan a route from its current location to opponents goal.This paper presents an artificial neural network based approach for path planning.The proposed approach takes the current state of the environment as an input and provides the best path to be followed as an output.The weights of the neural network have been optimized using three computational intelligence based techniques, namely evolutionary algorithms (EA), particle swarm optimization (PSO), and artificial immune system (A1S).To assess the performance of these approaches, a baseline search mechanism has been suggested that works on discrete points in the solution space of all possible paths.The performance of the base line and the neural networks based approach(es) is compared on a synthetic dataset.The results suggest that the neural network evolved via PSO based approach performs better than the other variations of neural networks as well as the baseline approach.
RoboCup Soccer Artificial neural networks Path planning Particle Swarm Optimization Evolutionary algorithms Artificial immune systems
Saleha Raza Sajjad Haider
Artificial Intelligence Lab, Faculty of Computer Science, Institute of Business Administration,Karachi, Pakistan
国际会议
4th international Conference,ICSI2013(第4届群体智能国际会议)
哈尔滨
英文
342-350
2013-06-12（万方平台首次上网日期，不代表论文的发表时间）