Plume tracking method of mobile robots based on improved sparrow search algorithm

Abstract:

To address the low plume tracking efficiency and excessively long search paths caused by the inability of robots to obtain reliable wind information, such as plume flow direction and flow velocity, in indoor plume diffusion environments, this paper proposed an autonomous plume tracking method for indoor robots based on an improved sparrow search algorithm (SSA). Firstly, inspired by the predation and anti-predation behavior of the sparrow population, this method used plume concentration as the fitness value of individuals so that the source-seeking robot could efficiently track the plume and locate the source position when it was not equipped with a plume flow direction sensor and a plume flow velocity sensor. Secondly, Logistic chaotic mapping was used to disperse the initial position of the sparrow population, and the elite solution of the sparrow population was retained to increase the population diversity. The Metropolis criterion was added to increase the probability of the algorithm escaping from the local extreme value area when updating the optimal solution. The improved A* algorithm was combined to optimize the search path. Finally, a plume tracking simulation comparison experiment was conducted, in which the improved sparrow search algorithm (ISSA) was compared with the genetic algorithm (GA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), and classic SSA, further validating the feasibility and effectiveness of the proposed algorithm in physical scenarios. The results show that compared with the aforementioned methods, the success rates of the proposed method increase by 31.00%, 4.84%, 1.34%, and 13.34%, respectively, and the search path lengths are shortened by 12.8,6.2,4.941, and 5.448 m, respectively. This study provides a new approach and reference for enabling efficient plume tracking by mobile robots in plume diffusion environments where reliable wind information is unavailable.