Adaptive Weight Adjustment and Searching Perception Strategy for Multivariate Complex Environments

Wenshan Wang,Jianguo Sun,Sizhao Li,Junpeng Wu,Qingan Da,Weizhi Meng

doi:10.1155/2022/6710661

Abstract

Affected by the complex environment and the destruction of communication infrastructure in the disaster-stricken area, it has brought great challenges to the search and rescue team. The use of small unmanned aerial vehicles (UAVs) for search tasks can minimize casualties. Therefore, in order to avoid any possible collision and search for unknown targets in the shortest time, it is necessary to design a multi-UAV cooperative target search strategy. In this paper, we analyze the unknown target search problem of multi-UAVs under random dynamic topology and propose an adaptive target search strategy based on the whale algorithm. First of all, each UAV detects the environmental information of its current area and uses the probability map search algorithm to gain the target existence probability map in the task area. Then, the whale optimization search method of shrinking circle or spiral is selected to update the position of the UAV to continuously approach the target. Finally, the obstacle avoidance strategy based on artificial potential field is designed to solve any collision problems that may be encountered during the flight of UAVs. Simulations on multi-UAVs target search in different scenarios show that compared with the whale optimization algorithm, the proposed algorithm can reduce the search time by 43.1% and the total path cost by 18.1%, and it is also superior to the advanced metaheuristic optimization algorithms such as PSO and GWO.

Highlights

Due to its high flexibility and autonomy, unmanned aerial vehicles (UAVs) can perform surveillance and search tasks in complex environments, especially in postdisaster search and rescue scenarios [1]
We propose an adaptive search strategy based on a whale optimization algorithm (ASWOA) for multi-UAVs to quickly find lost persons in disaster scenarios with less energy consumption
The contributions of this work are as follows: (i) The obstacle avoidance strategy based on virtual forces is used to improve the flight safety of multiUAVs and reduce repeated paths and energy consumption (ii) The probability map search strategy is used to update the probability of the target existence in the task area to improve the search ability of the population (iii) Adaptive inertia weight is introduced to balance the convergence speed of the algorithm (iv) The position adjacent to the UAV with the highest target existence rate is selected as the optimal solution to improve the convergence speed of the algorithm

Summary

Introduction

Due to its high flexibility and autonomy, UAVs can perform surveillance and search tasks in complex environments, especially in postdisaster search and rescue scenarios [1]. Jarray and Bouallègue [15] proposed a method based on GWO for flight path planning of UAVs to ensure destination arrival and obstacle avoidance, whereas this method has slow convergence speed and high overhead For this reason, Liu et al [16] proposed a whale optimization algorithm, which improved the global search ability of WOA by introducing adaptive weights and nonlinear convergence factors. (i) The obstacle avoidance strategy based on virtual forces is used to improve the flight safety of multiUAVs and reduce repeated paths and energy consumption (ii) The probability map search strategy is used to update the probability of the target existence in the task area to improve the search ability of the population (iii) Adaptive inertia weight is introduced to balance the convergence speed of the algorithm (iv) The position adjacent to the UAV with the highest target existence rate is selected as the optimal solution to improve the convergence speed of the algorithm.

Optimal Deployment of Multi-UAVs

Simulation Analysis

Findings

Conclusions