**1. Introduction**

With the constant progression of urbanization and industrialization, the mobility of rural young and middle-aged laborers has intensified [1]. The sustainable transfer of non-agricultural labor has led to a decline in agricultural labor, and the problem of aging of the agricultural labor force has become more serious [2]. For example, the proportion of agricultural production and management personnel aged 55 and over is as high as 33.6% according to the main data of the Third Agricultural Census Bulletin of China in 2017. Furthermore, as aging continues, the physical health of the elderly labor force continues to decline, which results in a significant reduction in the supply of effective rural labor and an adverse effect on agricultural output [3,4]. Most agricultural production tasks are labor-intensive and seasonally oriented projects that exacerbate the constraint of seasonal labor shortages [3–5] and increase the cost of agricultural labor. For example, according to a survey conducted by Zhen et al. [6], during the rural busy season, the labor cost of agricultural planting increased from 80 CNY per person per day in 2015 to 90 CNY in 2016 to 100 CNY per person per day in 2017, and the labor cost of technical agricultural labor is even higher [7]. Some statistics show that the agricultural unit labor cost in developed

**Citation:** Mao, W.; Liu, Z.; Liu, H.; Yang, F.; Wang, M. Research Progress on Synergistic Technologies of Agricultural Multi-Robots. *Appl. Sci.* **2021**, *11*, 1448. https://doi.org/ 10.3390/app11041448

Received: 31 December 2020 Accepted: 1 February 2021 Published: 5 February 2021

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countries such as Germany, Japan, and the United States decreased by 30.17%, 44.22%, and 23.44%, respectively, while agricultural labor productivity increased by 64.78%, 81.22%, and 34.83%, respectively, from 2005 to 2014. On the contrary, China's agricultural unit labor cost increased by 45.17%, and agricultural labor productivity, which is much smaller than that of developed countries, increased by 50.28% [8]. Therefore, the increasing cost of labor will lead to an increase in the cost of agriculture, which will result in a relative decrease in agricultural productivity and international competitiveness [9]. It is urgent to enhance the innovation of agricultural science and technology and replace extensive and expensive repetitive manual operations with intelligent agricultural machines or robots [10].

Due to the growing maturity of computer technology, sensor technology, and control theory, different types of agricultural robots have been developed based on characteristics of agronomy, such as fruit- or vegetable-picking robots, spraying robots, and harvesting robots. The agricultural robot can replace traditional human efforts to engage in all kinds of labor-intensive and complicated agricultural production activities and reduce the decline of output caused by improper human operation, negligence, inaccurate operation, and other reasons, as well as major physical injuries and even casualties of operators [11]. However, the operation efficiency of a single agricultural robot is too low and cannot meet the operation demand in busy seasons without coordination and cooperation by artificial auxiliary resources or other robots [12]. As early as 2009 and 2012, Johnson et al. [13], Moorehead et al. [14] and others in the United States replaced a single robot with a group of agricultural mobile robots to complete mud moss harvesting and orchard spraying successively with appropriate cooperative operation mechanism, which can reduce production costs and improve operational efficiency [15]. Therefore, to adapt to the increasing scale of production, meet the needs of social development, and narrow the gap with other international world powers with advanced scientific capabilities, it is necessary to research the relevant technology of agricultural multiple robot systems. This work focuses on the research progress of the cooperative operation, one of the key technologies of agricultural multiple robot systems.

There are many types of agricultural multi-robots, and this article mainly focuses on unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs). This paper summarizes the research progress on synergistic technologies of cooperative operation of the abovementioned multiple robots and characterizes the expected research of related technologies.

#### **2. Problems with Multi-Robot Applied to Agricultural Environments**

Crops are fixed in cultivation season and time, which determines the labor demand pattern of agricultural production in a year, and agricultural operators need to make flexible responses and treatments according to the growth pattern of crops, such as plowing, planting, management, and harvesting [16]. At the same time, to adapt to the development of agricultural intensification, scale, and industrialization, and to reduce the economic losses caused by untimely processing, a collaboration of multiple farm machinery operated by people has widely appeared in agricultural production (as shown in Figure 1).

In Figure 1a multiple rotary tillers are being used to plow rice fields on sloping land to safeguard food production and mitigate the impact of the phenomena of lack of labor resources, which is brought about by the New Crown epidemic [17]. In Figure 1b, multiple corn planters are employed to sow seeds in a large field, which saves labor and ensures the quality of seeding, and directly improves the yield and quality of corn planting [18]. In Figure 1c, multiple drones are used to spray pesticides in cotton fields, which could be targeted according to the types of pests and diseases, and also prevented their rapid spread in the early stages of infestation [19]. In Figure 1d, it has become a trend to manually operate multiple combines simultaneously during the wheat harvesting season to avoid the effects of rainfall on wheat quality and yield [20].

**Figure 1.** Man-operated farm machinery worked in a large field. (**a**) Multiple rotary tillers plowing rice fields in spring. (**b**) Multiple seeders sowing corn in spring. (**c**). Multiple drones spraying cotton fields in the summer. (**d**) Multiple harvesters harvesting wheat fields in summer.

By manually operating the machines, the quality of operation of the implements relies heavily on human experience, while the use of robots instead of manual operation can free up manpower and ensure the quality of operation. However, the topography, soil, light, and climate conditions of the crop growing environment are different from those of indoor and urban transportation environments, and these conditions pose a challenge to the application of multiple robots in agriculture.

Take the example of multiple harvesters harvesting grain in farmland. First, farmland is an unstructured environment, which means that the road conditions are undulating, there are various types of obstacles, and there are missing or blurred lane lines on the ground, and agricultural machines both share the same resource and interact with each other to become dynamic obstacles to each other. Secondly, agricultural operation tasks have strong requirements for operation time, such as harvesting grains in a fixed short time frame. Furthermore, the amount of grain output varies from plot to plot, and the number of agricultural machines needed and the number of operational tasks assigned to them is dynamically changing (such as harvesters with large loading capacity should match the plots with large grain output). Finally, even if the same type of farm machines work together, the characteristics of the machines are not the same (for example, the harvester with the same loading capacity, the fuel consumption is different, the harvester with high fuel consumption should be assigned the operation task more than its work cost, and its operation path should be as short as possible but the harvesting volume should be as large as possible). To ensure that multiple machines can cooperate, a multi-robot system is required to be able to organize multiple robots flexibly, quickly, and efficiently according to the changes in the environment and tasks, and to fully utilize the capabilities of each robot to finally complete the given task with high quality [21]. At the technical level, in addition to being accurately informed of the positioning information of the swarm and the environmental information of the operation, and solving collision and obstacle avoidance, it is also necessary to assign operational tasks to multiple machines, plan operational paths (such as in areas where multiple robots work together), coordinate the formation control of multiple robots, and maintain the information interaction between multiple robots.

#### **3. Research Progress on Synergistic Technologies of Agricultural Multi-Robots**

To solve the above problems, it is necessary to study technologies of collaborative operation, such as environment perception, task allocation, path planning, formation control, and communication-based on multi-robot architecture. Since each technology does not solve the same agricultural problems, this section first classifies the types of development of these technologies, then describes and reviews each of these cooperative technologies in terms of research methods or problem solving, and finally summarizes their research development status and characteristics.
