*3.6. Communication*

Communication is the basis of information interaction and collaboration among multiple robots. In agricultural production, many factors affect the fine operation of agricultural robots, and to maintain coordination and cooperation among multiple robots and to gain a more comprehensive understanding of the environment in which multiple robots perform tasks, robots need to interact with each other through information to better perform a given task [29]. Balch and Arkin concluded that even a small amount of communication can improve the performance of multi-robot systems tremendously through experiments [125].

At present, the communication technology of agricultural multi-robots mainly involves three parts: multi-robot communication mode, communication network, and communication protocol.

• Communication mode

The multi-robot communication mode is divided into three categories from a macro perspective: explicit communication, implicit communication, and explicit and implicit communication, as shown in Figure 17. Explicit communication is an interactive mode through communication as a medium, requiring a clear communication protocol between interacting parties. This method is often used for concordant communication among robots, but it incurs fairly large costs. Implicit communication is the acquisition of the required information through the external environment and internal sensors without an explicit exchange of data, so some advanced coordination strategies cannot be used, which affects the capacity to perform certain complex tasks.

**Figure 17.** Communication modes of multiple robots.

Since explicit communication and implicit communication have their advantages [120], explicit communication is used for the integrated control of robots in the upper layer, and implicit communication is used for integrated control of robots in the bottom level. Explicit communication means that the robot communicates directly or indirectly with other robots via wireless networks. For example, robot 1 sends a message to all robots in the communication range in broadcast communication, that is, without specifying a particular robot, robot 2, which does not need the message, receives the message. In implicit communication, the intermediary for inter-robot communication is often the surrounding environment. For example, the UAV can be informed about the farmland in advance and build a model of the farmland environment, and the ground robots operate on the ground based on this farmland model [122]. The combination of both communication modes can be used to develop their advantages, improve the flexibility to confront the various dynamic and unknown environments, and complete many complex tasks in agricultural production.

For the implicit communication of multiple robots, you can refer to Section 3.2 environment perceptions, here we focus on robot explicit communication techniques.

In literature [25], two aerial drones were equipped with GPS, visible and near-infrared spectral cameras, which took pictures of the farmland at a set series of ordered waypoints and uploaded them to the backend, which sends the processed information of weeds in the farmland to the ground robot. The ground robots were equipped with RTK-GPS, RGB camera, and LIDAR. RTK-GPS provided accurate heading for the ground robot, RGB camera detects weeds and crop rows, and LIDAR detects obstacles on the vehicle trajectory. While the ground robots were safely walking along their respective set paths, weeding operations start if the weeds detected by the cameras were the same as the weed information in the farmland. In this multi-robot system, the aerial drones and ground robots did not communicate directly but completed the cooperative operation through the interaction of environmental information.

• Wireless communication net of multiple robots

As shown in Figure 18, the agricultural multi-robots need to adjust their pose in real-time. Therefore, the data exchange of communication among multiple robots is mainly based on wireless communication technology in agricultural production. This technology mainly involves a wireless local area network (WLAN) and a wireless personal area network (WPAN), such as WI-FI, Bluetooth, ZigBee, and IRDA (infrared data association). Among them, WI-FI technology has been developed most rapidly in agricultural multi-robots.

• The wireless communication protocol of multiple robots

**Figure 18.** The computer as the center controller was used to send initial paths for UGVs and UAVs control units through communication. And perception data were exchanged between multi-robots and computers.UGV.

The wireless communication protocols are primarily used based on wireless communication standards and the unlicensed band. Taking the WLAN as an example, the IEEE 802.11 series standards and the 2.4 GHz or 5 GHz bands are used in this communication. The IEEE 802.15 series of transmission technology protocols are selected in WPAN.

Combined with the above communication technology, the research progress of agricultural multi-robots in communication in the past 10 years is summarized, as shown in Table 7.




**Table 7.** *Cont.*

<sup>1</sup> IEEE802.11a standard, operating in the 5 GHz band, has a data transmission rate of 54 Mb/s. <sup>2</sup> IEEE802.11b standard, operating in the 2.4 GHz band, has a data transmission rate of 11 Mb/s, and is not compatible with IEEE 802.11 a. <sup>3</sup> IEEE802.11g is a standard that increases the transmission speed of 802.11b from 11 Mb/s to 54 Mb/s. <sup>4</sup> IEEE 802.11p standard is a communication protocol expanded by IEEE 802.11 standard, which is mainly used in wireless communication of automotive electronics.

> In addition to the above wireless communication technologies, Albani et al. adopted a mobile ad hoc (peer-to-peer) network [128,129], which regarded the UAV as a communication node in the network and used three communication strategies (simple, flooding, geo-aware) to solve the communication problem of UAVs flying in the field. The simplest communication strategy is a single broadcast mode, that is, the source node sends information to the nearest node. Flooding constitutes a multi broadcast mode, that is, the source node sends information to multiple agents. Geo-aware employs a source node with the highest utilization rate, and this node sends the messages. All three communication strategies ignore communication errors and focus on the impact of the communication range and protocol on work efficiency. The simulation results show that the effective information of weed monitoring can be transmitted with a minimum number of UAVs under the geo-aware approach. However, the communication strategy discards new information obtained by UAVs of the distributed architecture, and messages cannot be effectively transmitted with a wide range of communication (such as over wide areas of farmland). Agricultural multi-robots working in the farmland often encounter signal occlusion, atypical weather, etc.

> Large agricultural multi-robots working in agricultural fields rarely encounter problems such as signal occlusion and atypical weather. However, in other agricultural products, such as greenhouse and orchard, when the size of the multi-robot is smaller than the height of the crop, its communication signal strength is extremely attenuated by factors such as crop planting, growth characteristics, planting scale, and weather (natural wind and rain). Previous references [126,127,130] showed that the test results of the WI-FI communication system of agricultural multi-robots suffered from WI-FI signal intensity attenuation largely because of the reflection and scattering effects of crops, and the effective communication distance was less than 50 m (far less than the theoretical communication distance of 300 m) in mature wheat fields, cornfields, and peach gardens. Therefore, it is a future research

direction to select suitable multi-robot communication technology according to the characteristics of crops and to carry out research on multi-robot communication patterns based on crop shape characteristics.

#### **4. Discussion**

In general, in the past 10 years, the synergistic technologies of agricultural multi-robots have achieved some research results, and multi-robot collaborative operations have been realized in specific agricultural scenarios. But with the increasing demand for agricultural operations, the following challenges in the application of multi-robots in agriculture still exist to be solved:

• Flexible agricultural multi-robot system architecture

Multi-robot architecture is the basis for collaborative operations of multi-robot systems. In the last decade, agricultural multi-robot systems have mainly focused on centralized or distributed architectures to accomplish collaborative operations under pre-defined conditions. Both architectures have their advantages and disadvantages, but as the number of robots increases (such as multiple aerial robots cooperating with multiple ground robots) and new agricultural operational needs increase (such as sampling in marine environments [131], cargo handling in hilly mountainous areas, pest control in orchards, etc.), it is clear that the scalability and flexibility of multi-robot systems relying on only one architecture are limited. The advantages of centralized and distributed architectures are combined to form a hybrid architecture, or the application architecture is dynamically selected according to the task attributes, which can overcome the low performance caused by the self-centeredness in the distributed architecture and reduce the lack of control flexibility in the centralized architecture.

• Fast and precise environmental perception

In environmental awareness, positioning and sensor fusion answer the question of "where am I" and building a map answers the question of "what's around me", and the answers to these two questions are the prerequisites for robots to start their operations. The positioning and sensor fusion technologies of agricultural multi-robot are mostly used in large fields with unobstructed outdoor signals, where the communication between robots is normal and the robots can get accurate positioning, heading, speed, obstacles, and other information based on their sensors. However, considering the severe compaction of soil by large agricultural machines, the compression of application costs, and the promotion of this concept of refined agriculture, light, and small agricultural robots will be the trend of future development, which will make multi-robot positioning unable to continue to rely on the high-precision positioning of a particular robot or a particular sensor (e.g., GPS). Especially in case of robot failure or communication failure, how to ensure the accurate positioning of the remaining individuals and make the multi-robot system with good robustness is a problem that needs to be solved urgently.

Mapping not only can accurately learn the information of detailed agricultural information, static obstacles, and the location of other robots but also can assign tasks and plan paths for multiple robots. The more accurate the agricultural information, the more accurate the operation objects will be, but this contradicts agricultural tasks that urgently need a fast response, which means that the time spent on the subsequent processing of information data reduces the real-time and flexibility of multi-robot operations. How to obtain dynamic agricultural information quickly and accurately and match it with the precise location of the operation object is another urgent problem in environment sensing.

• Reasonable task assignment in real-time

The task assignment is related to the multi-robot coordination and collaboration mechanism, and the simple zoning assignment of robots cannot adapt to the dynamically changing operational tasks. Also, the number of robots, operating time, and cost of robot operations need to be dynamically adjusted to the operating task. Even for the same type of robots, items such as fuel or electricity, fertilizers, herbicides, and pharmaceuticals can change differently depending on the target of the operation. It is impossible to obtain fast and reasonable response results based on the changes of these uncertainties even depending on a priori knowledge. How to enable multi-robots to timely self-adjust to dynamic task changes and obtain reasonable operational tasks or operational task sequences through real-time interaction with dynamic environments is an urgent problem for multi-robot task assignments in agriculture.

• Dynamic and reliable path planning

The path of agricultural multi-robot operations is designed to accomplish dynamic operational tasks, and the robot's travel rules are usually fixed. The global path of multirobot offline planning only considers fixed travel rules, such as the point-to-point method and image method, which can avoid static obstacles smoothly, but cannot be extended to be applied to similar agricultural scenarios. In particular, if the dynamics of the agricultural environment change rapidly (e.g., weeds are growing in the field after the rainy season) and the agricultural information is not fully known (e.g., the constructed mapping usually does not contain dynamic obstacles), fixed path planning cannot meet the needs of complex tasks (e.g., weeds are not on the planned path). Therefore, how to perform reliable path planning for multiple robots based on operational tasks with distinct temporal characteristics is a problem that needs to be solved for multi-robot path planning in agriculture.

• Flexible and robust formation control

Multi-robot formation control currently focuses mostly on robot swarms walking steadily along a straight line in a fixed formation. However, when a multi-robot system encounters unexpected events, such as robot failure, communication failure, or stopping travel due to dynamic obstacles, how to mitigate the impact on other robots, respond quickly, and adjust the robot formation shape to continue the task is a concern for agricultural multi-robot formations. Although some studies have shown that multiple robots can be selectively controlled based on time or event drivers, or by replacing the "leader" in the queue, none have been applied in real production.

• Communication system based on plant characteristics

Communication is the basis of multi-robot collaboration in agriculture, whether it is multi-robot positioning, collaborative control, or remote supervision, communication is indispensable. The agricultural environment lacks communication infrastructure construction, and most of them directly used industrial communication systems do not consider the relationship between outdoor plant growth and communication signals, and their communication range and signals will be attenuated to different degrees in the agricultural environment. Therefore, the construction of a communication system adapted to the agricultural multi-robot operating environment is a problem that needs to be solved for multi-robot communication.

#### **5. Conclusions**

Given the current challenges in agricultural multi-robot research, this paper points out future research directions in six areas to enhance the application of agricultural multirobots in practice. Firstly, to build a flexible and changeable agricultural multi-robot system architecture based on hybrid architecture so that the multi-robot system has good environmental adaptability and robustness. Secondly, to develop sensor information fusion technology among agricultural multi-robots based on mutual positioning methods to improve the positioning accuracy of multi-robots in agricultural environments without GPS. Meanwhile, SLAM technology for agricultural multi-robots is studied to rapidly build environment models to adapt to the dynamically changing agricultural information. Third, to introduce deep learning mechanisms in agricultural multi-robot task assignment enables multi-robots to self-identify, evaluate, compare, remember and adjust during their interaction with the environment, and adjust the way they interact with other individuals according to specific tasks so that the group as a whole is equipped with the ability to complete multiple types of tasks. Fourth, dynamic planning of multi-robot paths based

on a combination of centralized and distributed path planning methods enables multirobot systems to adapt to real-time changing operational tasks and avoid obstacles and other robots promptly. Fifth, to modify the reference points of multi-robot formations flexibly according to changing events, adjust the distance and direction between formation members, reduce the impact on other mobile robots, and complete operational tasks. Sixth, to study the relationship between plant growth characteristics and communication system, establish a communication signal attenuation model, and design an agricultural multi-robot communication protocol based on this model to build a communication system.

In summary, the multiple robot system represents the future of robot development. The synergistic technologies for the research of agricultural multi-robots have a great value and bright prospects but are also extremely challenging. Therefore, it requires participation by researchers to combine the former research results, recognize the developing trends, and use practicality as the ultimate goal to drive forward the coordination technology of agricultural multi-robots.

**Author Contributions:** Writing—review and editing, W.M.; supervision, H.L., Z.L., F.Y. and M.W.; project administration, F.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received funding from the Major Science and Technology Project of Shaanxi Province of China (Program No. 2020zdzx03-04-01).

**Institutional Review Board Statement:** The study in the paper did not involve humans or animals. The project supporting the research of this paper is to study agricultural machinery and equipment for apple orchards, and the research does not involve humans or animals.

**Informed Consent Statement:** The study in the paper did not involve humans.

**Data Availability Statement:** This paper is a scientific review paper that provides a detailed analysis and summary of agricultural multi-robots, independent of the data.

**Acknowledgments:** Authors thank the funding received from the Major Science and Technology Project of Shaanxi Province of China (Program No. 2020zdzx03-04-01). We also thank the critical comments and suggestions from the anonymous reviewers for improving the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
