# Multitarget Search Algorithm Using Swarm Robots in an Unknown 3D Mountain Environment
## 1 Introduction  
A large number of studies are devoted to swarm robot multitarget search, which is widely used in postdisaster search and rescue, natural resources exploration, enemy position detection, underwater fishing, and other application fields [1]. [PERSON] et al. mapped particle swarm optimization (PSO) well with the target search process [2]. [PERSON] et al. deployed the cooperation and competition to solve the spatial conflicts of swarm robots [3]. [PERSON] et al. introduced a probability-constrained finite state machine to effectively resolve individual resource conflicts and improve the efficiency of target search [4]. Taking UAV as the carrier, [PERSON] proposed a 3D adaptive inertia weight extended particle swarm optimization (IAEPSO) to realize the search of air targets in a mountain environment [5]. In order to search for the lost object, [PERSON] et al. transformed the problem of target search into a probability problem based on the location of the last lost object and the creation of a Bayesian map, and proposed motion-encoded particle swarm optimization (MPSO) [6][PERSON] Aiming at the target search of underwater vehicles, [PERSON] et al. proposed a multi-AUV collaborative team integration algorithm, which has the advantages of fewer parameters and no speed jump [7]. In order to reduce the communication pressure of swarm robots, [PERSON] et al. realized the information interaction among swarm robots through indirect communication [8]. [PERSON] et al. assumed that the target was discovered when it was within the detection range of individual UAV, and then proposed an ergodic target searchmethod; under the background of this method, [PERSON] et al. also proposed an approach to increase or decrease the number of UAV individuals [9; 10][PERSON]  
The above research shows that existing research on swarm robot multitarget search is mainly aimed at 2D plane environments or a 3D environment with constant height [11; 12; 13; 14]. However, in the practical application of environmental detection and postdisaster [PERSON], swarm robots may face complex mountain environments. For multitarget searches in 3D environments, many studies have implemented UAVs. For example, [PERSON] [15] proposed a task planning strategy of a UAV swarm in a 3D environment for landslide monitoring and postdisaster search for survivors. Inspired by the gray wolf tracking strategy, [PERSON] [16] proposed an adaptive formation tracking control method applied to a UAV swarm system, which improved the system stability and accuracy of formation tracking. [PERSON] [17] customized a UAV interactive decision-making mechanism that could switch the interaction method according to the distance between aircraft during a search for a cooperative UAV swarm search task under the condition of limited communication distance and realized search path planning in a dynamic environment. In view of the realistic environment faced by swarm robots in a targeted search, the premise of their search is to move safely in the task environment, so it is particularly important to consider the obstacle avoidance problem. [PERSON] [18] proposed UAV path planning based on an improved 1 artificial potential field to efficiently solve the UAV obstacle avoidance problem. [PERSON] [19], in view of the traditional artificial potential field method in a complex environment and the problem of low efficiency of obstacle avoidance, proposed a traditional artificial potential field method as an improved potential field function and improved the traditional spherical potential field for the ellipsoid potential field. The experimental simulation proves that the improved artificial potential field method provides efficient and safe UAV obstacle avoidance path planning in a complex 3D environment. A UAV has the advantages of information sharing, strong system survivability, and cost performance, which can better meet the needs of a targeted search in 3D space. However, it has some problems for ground targeted search in complex mountain environments.