###### Abstract  
Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed. agricultural robots; complete coverage path planning; Markov chain; improve genetic algorithm +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]. successfully avoid dynamic and static obstacles, but the mode was more complex and the calculation amount of the algorithm was very large. [PERSON] et al. [5] utilized the improved A* (A-Star) algorithm for complete coverage path planning of the sweeping robot, which effectively enhanced the operating efficiency of the algorithm and reduced the length and repetition rate of the path planning. [PERSON] et al. [6] studied cleaning robots and proposed a template-based method to control path execution, integrating the kinematics of mobile robots into the path planning process. However, the template method relies on preset path patterns, lacks flexibility, and is difficult to adapt to complex or irregular environments, leading to low coverage efficiency. To solve the problem of bulldozers easily falling into dead zones during unmanned operations, [PERSON] et al. [7] added machine operation rules to the bio-inspired neural network algorithm to reduce path repetition rate, and used the A * algorithm to help bulldozers quickly escape when the machine is stuck in dead zones. [PERSON] et al. considered the height variation and energy consumption factors in the full coverage path planning for horticultural tractors in mountainous areas [8]. [PERSON] et al. [9] proposed a traversal algorithm that completely covers the work areas based on the spanning tree coverage algorithm. However, this method has a high repetition rate and a high number of robot path turns; [PERSON] et al. [10] proposed a low repetition rate complete coverage path planning algorithm using an ant colony algorithm. However, the total path planned by this traversal algorithm is relatively long, and it is prone to falling into dead zones when encountering areas with dense obstacles. The genetic algorithm (GA) is a well-known, meta-heuristic technique for addressing static mobile robot global path planning (MRGPP) issues [11]. However, genetic algorithms have certain shortcomings, such as a simple evolutionary process, inefficient population initialization, and a lack of prediction for the trend of random event changes. Therefore, robots with different job requirements will improve their path planning using GA based on specific operation requirements and environments. In automated guided vehicles (AGV) path planning by GA, [PERSON] et al.