Book Chapter or Conference Paper Title
Path planning in robocup soccer simulation 3D using evolutionary artificial neural network
Faculty / School
Faculty of Computer Sciences (FCS)
Department of Computer Science
Was this content written or created while at IBA?
International Conference on Swarm Intelligence
12-15 June 2013
Springer, Berlin, Heidelberg
Abstract / Description
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 ball, 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 (AIS). 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.
Raza, S., & Haider, S. (2013, June). Path planning in robocup soccer simulation 3D using evolutionary artificial neural network. In International Conference in Swarm Intelligence (pp. 342-350). Springer, Berlin, Heidelberg.
Raza, S., & Haider, S. (2013). Path planning in robocup soccer simulation 3D using evolutionary artificial neural network., 342-350. https://doi.org/10.1007/978-3-642-38715-9_41