An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method.
N. Zhang and D. C. Wunsch, "A Comparison of Dual Heuristic Programming (DHP) and Neural Network Based Stochastic Optimization Approach on Collective Robotic Search Problem," Proceedings of the International Joint Conference on Neural Networks, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1223352
International Joint Conference on Neural Networks, 2003
Electrical and Computer Engineering
Keywords and Phrases
DHP; Collective Robotic Search Problem; Dual Heuristic Programming; Expenses; Heuristic Programming; Mobile Robots; Multi-Robot Systems; Neural Nets; Neural Network Algorithms; Neural Network Based Stochastic Optimization Approach; Noise; Optimisation; Search Problems; Stochastic Systems; Target Source Location; Task Performance
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2003