*3.1. Architecture of Agricultural Multiple Robot Systems*

A reasonable architecture can guarantee information flow and control flow in the agricultural multi-robot system and make effective cooperation among multiple robots possible [22]. At present, the architecture of agricultural multi-robots can be divided into centralized architecture and distributed architecture. It is found that the earliest recorded structure of agricultural multiple robot systems comes from hay harvesting and transportation robots in farmland [23,24], for which these multi-robot systems were operated under the principle of centralized architecture. As shown in Figure 2a, in leader-follower mode, a relatively powerful robot is selected as the "leader" of the swarm robots, performing specific motion planning for the remaining robots after analyzing and processing the sensory information, but these remaining robots are just executors, without the ability to choose their actions or coordinate with each other. Alternatively, as shown in Figure 2b, in the central controller mode, each robot can perform tasks independently and is commanded by a central controller [25]. The advantage of this centralized architecture is that the theoretical background is clear, and the implementation is intuitive, but the flexibility, fault tolerance, and adaptability are poor [26].

**Figure 2.** The centralized architecture of an agricultural mobile multi-robot system. (**a**) One of the guided following modes, where the slave robot follows the travel path taken by the active robot [27]. (**b**) Another of the guided following modes, where the slave robot is ordered by the master robot to go the other way [27]. (**c**) The centralized control mode is usually a back-end computer that monitors, plans, and controls the robot's tasks and operating paths.

Compared with the operating environment of UGVs, the operating area of UAVs has the advantage of no obstacles, so these systems generally adopt the distributed structure [28]. As shown in Figure 3, the three UAVs in the multi-robot system carry out agricultural situation monitoring in the individual workspaces independently; each robot

of this system had a high degree of autonomous operation ability and can complete a given task according to its aims; robots can communicate with each other, exchange information, and coordinate their behaviors equally and independently to complete a given task [28]. This structure has strong scalability and certain advantages in real-time operation, fault tolerance, and reliability, etc. [29] and is suitable for handling tasks related to spatial states [30]. However, "it costs a lot in terms of the coordination mechanism, such as task allocation and motion planning" [29].

**Figure 3.** The distributed architecture of an agricultural mobile multi-robot system (**a**) Agricultural multi-robot agricultural condition monitoring in different areas of the vineyard [28]. And the robots exchange their information for autonomous work. (**b**) Distributed structure diagram of a multi-robot. Each robot can exchange information with other robots through communication and make decisions autonomously [26].

The centralized architecture that can be divided into a leader and follower robots is suitable for highly coordinated tasks and is advantageous in a fully known environment. The distributed architecture, in which there is no affiliation among robots, is suitable for weakly coordinated tasks and is advantageous in large-scale, complex, and varying environments.

#### *3.2. Environment Perception*

Environmental perception is the premise of the cooperative operation of multiple robots in agriculture. That is, the mobile carrier can use the sensors carried by itself (these sensors include internal sensors and external sensors [31], where the internal sensors include odometers, magnetic compasses, inertial navigation, and global positioning systems to determine the speed, position, and direction of the robot in the environment; external sensors include ultrasound, infrared, laser and vision, used to sense surrounding information [26]) to obtain the information of the surrounding environment, extract the effective feature information within the environment for processing and analysis, and finally establish the environment model [32]. This technology mainly involves collaborative positioning, data fusion, and environmental construction.

#### 3.2.1. Co-Location Technology

The concept of co-location was originally proposed in 1994 by Kurazume and Nagata in Japan [33] The concept refers to a robot "sharing" its positioning results with other mobile robots and to other robots using this shared information to integrate their calculation results to improve the accuracy of positioning themselves and, in turn, sharing their positioning results with other mobile robots, repeatedly achieving the precise positioning of mobile robots [34]. According to the collaborative positioning method, this approach can be divided into active positioning, passive positioning, and interactive positioning [35]. However, no research on interactive localization has been reported in the literature of agricultural multi-robots.

• Active positioning

In the absence of information exchange, the robot relies on its sensors to obtain relative distances and angles by observing the neighboring robots for self-positioning [35,36]. As shown in Figure 4, the leading robot guides the follower robot to steer along the leading robot reference route [37].

**Figure 4.** Active positioning model.

For example, the black-and-white checkerboard feature board was fixed to the leader robot as the following feature, and the 3D information of each corner point on the blackand-white checkerboard feature board was obtained by the binocular vision camera fixed to the following robot, and the information was analyzed to finally obtain the longitudinal spacing, lateral offset and heading declination of the following robot relative to the leader robot. Using this navigation information to realize the automatic following of the following vehicle, the following system of master-slave orchard operation vehicle is established [38].

• Passive positioning

In an environment where information exchange exists, the robot indirectly obtains the relative distance and angle through the "observed" data provided by the friendly neighboring robot to perform its positioning [35]. As shown in Figure 5, the follower robot dynamically creates a reference heading for itself from the position point of the leading robot [39].

**Figure 5.** Passive positioning model [39].

Passive positioning mode is usually used in combination with kinematic control models and is one of the most used in master-slave robots. For example, GPS positioning was used for multiple robots, and the travel trajectory of the pilot robot was the main one. Under the premise of communicable, the pilot robot estimated the motion trajectory of

other robots using the kinematic model, and the difference between the sensor positioning and the estimated positioning value was calculated by the other robots or the pilot robot, and the lateral and longitudinal displacement of each robot was adjusted according to the difference [40].

However, the above positioning methods are highly dependent on the positioning accuracy of the leader robot, which requires good stability and robustness. When the leader robot fails, it is easy to cause the localization accuracy of the whole team to drop, or even the localization fails. In contrast, the interactive localization approach has an environment of information exchange, where robots achieve joint localization through these steps of mutual observation, data exchange, and information fusion [41]. In this case, the robots are in the same position in the team and there is no master-slave distinction, namely, they do not depend on the positioning accuracy of a fixed robot, and this type of phenomenon is reduced. The above co-location methods are summarized as shown in Table 1.


**Table 1.** Comparison of co-location methods.

It can be seen from Table 1 that the cooperative localization technology for agricultural multi-robots mainly adopts active localization and passive localization, which are computationally small and easy to implement. The real concept of "co-location" should be interactive positioning; through communication among robots, information sharing can be realized, and then the robot's positioning error can be corrected to achieve accurate positioning. However, this method incurs a large amount of calculation and a large complexity of the algorithm, making it difficult to implement. No examples have been found in the literature for agricultural multi-robots.

More practical applications indicate that the cooperative positioning of multiple robots in agriculture is replaced by a central controller or task manager coordination mechanism [49,50]. That is, the working area and travel path of each robot are planned by a task manager or central controller based on the established environment map. The work areas and work paths of these multiple robots usually do not intersect and each robot only needs to localize and navigate based on its sensors. For example [27], the vineyard area was divided into three UAV-monitored vineyard areas using the task manager according to set rules, and path planning was performed for the sub-areas of individual UAV operations, with each UAV flying at a different altitude to avoid collisions between the UAVs.

### 3.2.2. Data Fusion Technology

A single sensor has certain limitations. For positioning accuracy and reliability, it is necessary to utilize the advantageous features of each sensor, that is, data fusion of multiple sensors. Data fusion technology, also known as multi-sensor information fusion technology (MSIF), essentially involves the comprehensive processing of target information originating from different sensors at different times or multi-target information simultaneously, to obtain more accurate positioning, identification information of the measured environment or object, and comprehensive and timely assessment of the current situation [51,52], thus facilitating the subsequent planning and decision making of the robot. The strength of the data fusion capability directly affects whether the robot can effectively achieve mutual coordination and collaborative work.

The internal sensors in the agricultural robot mainly include a global positioning system (GPS), inertial measurement unit (IMU), steering angle potentiometer, and encoder, which are used to provide the robot with the position, heading, and steering angle information; the external sensors mainly include various LIDAR and cameras, which are used to avoid obstacles and collect environmental information, as shown in Figure 10. Among them, GPS can provide a unified coordinate system and accurate position information for field robots and is used most frequently [53]. With the promotion of satellite positioning technology, agricultural robots equipped with GPS positioning and navigation systems will become increasingly popular. Take the automatic navigation system System150 researched by TOPCON company of Tokyo in Japan as an example [54], the system adopts GPS-based advanced inertial guidance and terrain compensation technology, which can realize navigation in complex terrain environments with ±2.5 cm accuracy for straight line and turn. However, data fusion techniques are still very important for robot localization when GPS cannot obtain accurate position information in greenhouses or forests [55], or when the robot is too small to install high-precision sensors.

For example, to obtain information on the ambient temperature, humidity, light, and CO2 concentration in a greenhouse [56], a human remotely operated UAV was first operated to obtain a map of the greenhouse environment, and then the ground robot fused IMU, GPS and odometer information to output the actual location information of the robot through the extended Kalman filter (EKF) algorithm. In practical applications, because of the poor GPS signal in the greenhouse, the EKF was used to fuse the odometer and IMU. EKF is used to linearize the nonlinear system at the reference point using Taylor expansions, and then Kalman filtering theory is used to achieve the prediction and correction of the system. But the EKF still cannot solve the global localization problem [57].

Another example is an agricultural spraying multi-robot [58] that used particle filtering to fuse information from multiple low-cost sensors of the odometer, IMU, wheel encoder sensors, and GPS, which incorporates the open-source library RTKLIB and correction signals. The particle filter could also determine robot attitude based on a series of particles in noisy environments and when the GPS was offline. Particle filtering [59] is a basic method based on Bayesian filtering theory (the robot can determine the poses with a certain degree of confidence based on all available information [60]) and differs from Kalman in using particle sets to describe the probability distribution. However, a large number of particles need to be maintained for higher localization accuracy, which will consume a large number of computational resources, especially as the walking distance gets farther, which will put greater pressure on the computing platform with limited memory resources.

According to the sensor information fusion processing hierarchy, the technique is divided into three levels, namely the data layer, the feature layer, and the decision layer [61]. Their specific scopes, characteristics, and fusion algorithms are shown in Table 2.

From Table 2, it can be seen that information fusion in agricultural multi-robots is focused on multi-sensor information fusion of single robots, and there is almost no research on sensor information fusion between homogeneous or heterogeneous agricultural multi-robots. To obtain more information, the information collected by different types of sensors on a single robot is mostly different (e.g., GPS collects position information, IMU collects robot heading, odometer, etc.), but some information has a mutual transformation relationship (e.g., the integration of velocity from odometer can give distance), which can be regarded as the same kind of sensors at this time. The fusion algorithms are mainly based on classical EKF and particle filtering.


**Table 2.** Comparison of multi-sensor information fusion processing levels [62,63].

#### 3.2.3. Mapping

Once the multi-robot has determined its position, it also needs to determine information about the multiple robots' surrounding environment, for instance, the presence of obstacles. Mapping is the task of accurately describing the spatial position of the robot working environment and various objects (such as obstacles and road signs) in the environment, that is, to establish a spatial model (two-dimensional or three-dimensional) or map [26]. The purpose of creating this map is to provide path planning for the robot, so the map must be easy for the robot to understand and computationally manipulate and accommodate revision when new environmental information is detected [64].

At present, many methods have been developed for constructing environmental models for multiple robots, which are mainly summarized in the three types of grid-based model, geometric mode, and topological mode [22]. Probably due to the low environmental information of the topological model, no literature was found on the use of multiple robots in agriculture. On the contrary, the grid model and the geometric model provide abundant information about the agricultural environment, the purpose of multi-robot operations is clear, and more literature is applied to agricultural operations. And the detailed environment model facilitates the task allocation to multiple robots, real-time observation of multiple robots' motion, effective coordination mechanisms, and detection of robot motion faults.
