A study of robotic search strategy for multi-radiation sources in unknown environments

Abstract

This paper focuses on the multiple radiation source search problems, where the mobile robot identifies the number and parameters of sources online while exploring an unknown environment. The radiation superposition and the limited observations improve the difficulty of estimation, and the exploration trajectory is also associated with estimation. A novel search strategy based on receding horizon planning is proposed, which includes the observation, estimation, and exploration modules. The observation module filters and records the radiation intensity for estimation. In the estimation module, an adaptive differential evolution algorithm is integrated into the peak suppression particle filter to avoid the local optimum. The multi-source radiation gain model is conceived to determine the observation position in the exploration module. The strategy trades off exploration of unknown areas and exploitation of known radiation fields. The results of simulations and experiments demonstrate that the proposed strategy can identify the parameters and quantities of all sources in multi-modal radiation fields. Furthermore, our strategy exhibits superior performance in searching for multi-radiation sources in unknown environments compared with the boustrophedon path and the Next-Best-View planner.