Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications

Abstract

In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly.