<title>Comparative study of neural-network-based learning strategies for collective robotic search problem</title>

摘要

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. A variety of optimization algorithms can be employed to locate the target source which has the maximum intensity of the distribution of illumination function. It is very important to evaluate the performance of those optimization algorithms so that the researchers can adopt the most appropriate optimization approach to save a lot of execution time and cost of both collective robots and human beings. In this paper we provide three different neural network algorithms: steepest ascent algorithm, combined gradient algorithm and stochastic optimization algorithm to solve the collective robotics search problem. Experiments with different pair of number of sources and robots were carried out to investigate the effect of source size and team size on the task performance, as well as the risk of mission failure. The experimental results showed that the performance of steepest ascent method is better than that of combined gradient method, while the stochastic optimization method is better than steepest ascent method.