# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception
#### 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 \(\pm\)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 CO\({}_{2}\) 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.