A Path Optimization Algorithm for Multiple Unmanned Tractors in Peach Orchard Management

Abstract

In order to improve the management efficiency of peach orchards, this paper considers the cooperative operation scheme of multiple unmanned tractors. According to the actual situation, this paper constructs the path planning model of multiple unmanned tractors in a standard peach orchard, designs the objective function to optimize the total turning time and total operating time according to the tractor driving parameters, and solves it by improving the differential evolution algorithm. Aiming at the premature convergence problem, the permutation matrix is introduced to represent the driving paths of multiple unmanned tractors. Then, the dynamic parameters are adopted to make the parameters change with the number of iterations, and the elite selection strategy is used to eliminate the redundant feasible solutions. An Adaptive Elite Differential Evolution (AEDE) algorithm suitable for multi-tractor path optimization is proposed. The results show that, compared with the traditional Differential Evolution algorithm (Differential Evolution, DE), the total turning time and total operating time in the rectangular peach orchard optimized by AEDE are reduced by 3.34% and 0.87%, respectively. Compared with the block operation, the total turning time and total operating time of the AEDE-optimized rectangular peach orchard operation path were reduced by 37.37% and 9.47%, respectively. Experiments show that AEDE, which optimizes the operating path of multi-tractors in standard peach orchards, is able to improve the efficiency and reduce the operating time.

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

“…In addition, the parameter 𝑞𝑞, used for pseudo-state 𝑧𝑧 𝑝𝑝 , was 𝑞𝑞 = 2 in the simul without further explanations. Indeed, the value of 𝑞𝑞 can affect the performan EfiMPC defined in Equation (9). If 𝑞𝑞 = 0, EfiMPC will be degraded into tracking N a small value of 𝑞𝑞 has the potential to improve the agricultural machinery opera efficiency to a small extent, while the tracking performance can also be promised w high accuracy; a large value of 𝑞𝑞 has the potential to improve the agricultural mach operational efficiency to a larger extent, while the tracking performance (like lateral ations) may suffer accordingly.…”

“…The farming tasks were not specifically defined in this paper; instead, they are indicated by the constraints defined in Equation (9). Thus, once the constraints in Equation ( 9) were satisfied, it was assumed that the corresponding farming tasks had also been completed successfully.…”

“…A significant amount of research has been dedicated to the development of advanced algorithms for path planning in arable farming [8]. For example, Han, et al [9], proposed an Adaptive Elite Differential Evolution (AEDE) algorithm, which is suitable for multi-tractor path optimization, and can reduce the total turning time and the total operating time of the vehicle. Nørremark, et al [10], proposed an Artificial Bee Colony (ABC) algorithm for the capacitated vehicle routing problem, to obtain a coverage path-planning optimization method; its overall objective was to minimize the costs in time and distance of all vehicles featuring a harvest operation.…”

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

“…Promoting the development and popularization of unmanned agricultural tractors is crucial for improving land utilization efficiency and ensuring food security. The unmanned agricultural tractor (UAT) enables smart, standardized, and information-based operations [2]. It saves labor and avoids variable work quality caused by differences in operator skills.…”

Autonomous Driving Strategy for a Specialized Four-Wheel Differential-Drive Agricultural Rover

“…Many works focused only on the path-planning phase [34]. Han et al [35] proposed an optimized pathplanning strategy for a fleet of multiple unmanned tractors in peach orchards in order to improve the operating time, whereas Hameed developed a path-planning algorithm to improve fuel consumption [36]. Furthermore, some studies have applied planning strategies that take into account the condition of the soil [37,38] or robot dynamics [39].…”