# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 1 Introduction  
Swarm robots are composed of a large number of simple agents with autonomous capabilities, local communication, and perceived energy. Through a few simple rules, the whole system can show complex swarm behavior characteristics such as self-organization and cooperation [1]. Swarm robots were inspired by the many biological species that demonstrate collective behavior in nature [2; 3; 4]. In this kind of collective behavior, local interactions between individuals and the environment lead to a set of independent agents in a distributed way to solve complex tasks, without the central control unit. The locality of interaction and communication between robots may be seen as a limitation, but it has a good effect on the scalability and robustness of the system and is therefore generally superior to the use of global communication and global perception. Wireless sensor coverage can be widely used in forest monitoring [5; 6], industrial monitoring [7; 8], battlefield monitoring [9; 10], disaster prevention [11; 12], and other application fields, by deploying a large number of wireless sensors in some specific task areas to form a network to collect the data. Using large-scale swarm robots to replace sensors for regional coverage will improve the coverage rate of the area, while the robots can patrol a local area [13; 14; 15]. At the same time, the swarm robotic system also has better robustness and scalability for regional coverage. The neural network (NN) method has been widely used in robot motion planning, control and coverage [16; 17; 18]. [PERSON] et al. [19] proposed a neurodynamic method for multi-robot region full coverage navigation and designed a bionic neural network to build a workspace model and guide the multi-robot to complete the coverage task. The method does not needto know the environment or any predefined template in advance, and it can be completed automatically without any manual intervention, which has high computational efficiency and flexibility. However, most neural network methods are only applicable to a single robot for region coverage [20; 21]. Moreover, some neural network models need to learn programs, which have a high computational cost and are difficult to achieve real-time regional coverage. [PERSON] [22] introduced three basic types of multi-robot system coverage: blanket coverage, barrier coverage, and sweep coverage. Blanket coverage means that enough robots can be statically covered in the whole target area. Barrier coverage is made by placing multiple robots in a barrier that can be detected when a moving robot passes through the barrier. Sweep coverage is to plan a suitable path for multiple robots at the same time and to access every part of the area by scanning. Many researchers use different approaches to solve the problem of area coverage and pattern formation in swarm robots. Behavior-based methods and potential field methods are often used in these models [23; 24; 25]. Other approaches include the virtual structure strategy [26; 27]and the leader-follower approach [28; 29; 30]. However, in all of these methods, the computational cost increases significantly with the size of the group [31]. A pattern formation method for regional formation using the robot population was proposed in [32], which can adapt to the environment change caused by unknown target movement or obstacle. However, these methods use global coordinates and need to know the shape of the region. [PERSON] et al. [33] proposed a simple method to solve the problem of collaborative coverage of unknown two-dimensional space by a group of anonymous mobile robots. This controller is suitable for robots lacking calculation or storage. However, the coverage rate and the coverage uniformity cannot be guaranteed. [PERSON] et al. [34] proposed a coverage method based on visibility awareness control, which introduced a new concave division technology, and extended the power diagram to the power diagram based on visibility. This method has achieved good results in the problem of concave surface coverage.