Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM

Abstract

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.