Autonomous driving and operation control method for SF2104 tractors

Abstract: To solve the critical shortage and the increasing cost of rural labor, the concept of "one person, multiple machines" were proposed and an autonomous driving and operating system for SF2104 was developed. The hardware of the system included SF2104 tractor with a power reverser transmission and wire-controlled chassis, WAS-3106 angle sensor, 1SZ-230 subsoiler, GNSS (Global Navigation Satellite System) based auto-steering system for agricultural machinery (FARMSTARF2BD-2.5RD), SF9507 vehicle controller, and mobile monitor such as smartphone and PC (personal computer). The control system mainly included three function units, i.e., data acquisition unit, planning and control unit, and movement unit. The navigation and control method was deployed in the planning and control unit according to the hierarchical control method. The entire method constituted of the layer of navigation planning, the layer of behavior control, and the layer of behavior execution. The operation width, the turning radius and the first operation path (AB straight line) from user inputs were transferred to the layer of navigation planning, and it also used to calculate the path network data. The path network data, wheelbase from user inputs and the real-time data (i.e.，location, heading and front wheel angle), were transferred to the layer of behavior control involving the target behavior decision. The decision of the target behavior wouldl be transferred to the layer of behavior execution, which derived the target front wheel angle, the target engine rotation speed and the target implement position. The layer of navigation planning generated the path network data to meet the requirement of operating in the field and turning in the headland through the FSP (First Turn Skip Pattern). The layer of behavior control made the decisions of target behavior, including lateral control, speed control, turning control, lifting control, current path update and operation ending. When the tractor entered the operating strip, the system identified the starting point of the operation, and sequentially executed the behavior of implement lowering, the behavior of speed increase, and the behavior of tracking the AB straight line. When the tractor finished the operation of the current path, the behaviors of implement lifting, speed reduction, and turning were executed sequentially. The behavior of speed control was executed by controlling the tractor's engine rotation speed at a high value or a low value through the vehicle controller. The behavior of lifting control was executed by transmitting an implement status value to the controller of the hydraulic lifting system. The behavior of turning control was executed by transmitting a fixed front wheel angle which was calculated by tractor kinematics turning distance. The subsoil operation experiments were carried out in the Shunyi District of Beijing. The experiments included the manual driving group and the autonomous driving group. For the autonomous driving group, the operating trajectories were straight and smooth, the average standard deviation of lateral deviation was 4 cm, the average operating speed was 1.66 m/s, and the standard deviation of operating speed was 0.09 m/s. During the stable operating stage in the field, the standard deviation of engine rotation speed was 7.9 r/min, and the range of the average implement position was 23.8. For the manual driving group, the operating trajectories were not smoother than the trajectories of the autonomous driving group, and the average standard deviation of lateral deviation was 8 cm, the average operating speed was 2.98 m/s, and the standard deviation of operating speed was 0.27 m/s. The stability of engine rotation speed and the range of implement position were also poor in manual driving group. The results showed that the autonomous driving group outperformed the manual driving group in terms of operating accuracy and working stability, which can effectively reduce labor costs. This research provides a platform foundation and theoretical basis for the future research of multi-vehicle and multi-operation collaboration with less human operations.