Indoor Inspection Robot Positioning and Navigation Method Based on Improved Monte Carlo Localization Algorithm

Traditional SLAM and visual navigation technologies are difficult to maintain high accuracy in complex greenhouse orchard environments, mainly facing challenges such as dense fruit trees and light changes. In order to solve these problems, this paper proposes a method based on improved Monte Carlo Localization (MCL) algorithm and dynamic A path planning. The improved MCL algorithm optimizes the positioning efficiency by dynamically adjusting the number of particles and modifying the environmental characteristics. The improved A algorithm optimizes the path through environmental adaptive heuristic functions and dynamic programming, which achieves a shorter path and higher computational efficiency. Experimental results show that the proposed method is superior to the traditional method in terms of positioning accuracy, path planning and coping with dynamic environment, and is suitable for automatic inspection tasks in greenhouse orchards.