Abstract: The path planning of farm inspection robots is the key to realize intelligent monitoring of large-scale farms. Aiming at the problem of finding the optimal charging route during robot inspections, an improved ant colony algorithm is proposed. This algorithm uses the global information of the working environment to establish a target attraction function, guides the ant colony to choose the best path to reach the target point, and reduces the iteration time of the algorithm. By adding additional pheromone update items and improving the pheromone volatilization coefficient, the global search capability of the algorithm is enhanced to avoid the premature convergence in the later stage of the algorithm search and falling into the local optimum. Simulation experiments in simple and complex environments show that, compared with the classic ant colony algorithm, the algorithm has faster convergence speed and good stability, and can quickly converge to the best path.

Key words: mobile robot, path planning, ant colony algorithm, target attraction function, adaptive ,