Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A∗

Abstract

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A∗ (IACO-A∗) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A∗ algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A∗ has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A∗ algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A∗ algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.