Ricardo Huaman

In recent years, autonomous mobile robots have been increasingly employed in various domains due to their ability to navigate unpredictable environments with limited human intervention. The Robot Operating System (ROS) Navigation Stack is a contributor to this development. The present research assessed the effectiveness of the Lightweight and Ground-Optimized Lidar Odometry and Mapping (LeGO-LOAM) when integrated into the ROS Navigation Stack for a skid-steering mobile robot operating in two distinct settings. Performance was benchmarked against well-established algorithms: Gmapping handling mapping and AMCL addressing localization. The mapping experiment demonstrated that both Gmapping and LeGO-LOAM generated precise occupancy grid maps, with LeGO-LOAM providing finer environmental details due to its 3D point cloud projection. In navigation tasks, the Absolute Pose Error (APE) metric revealed AMCL’s marginally superior performance in the first environment, achieving an RMSE value of 1.637 against LeGO-LOAM’s 1.696. Conversely, in the second environment, LeGO-LOAM outperformed AMCL by recording a lower RMSE of 1.976, compared to AMCL’s 3.163. These findings support LeGO-LOAM’s viability as an alternative tool suited to localization and mapping tasks within the ROS Navigation Stack and emphasize the significance of selecting algorithms tailored to specific environments to achieve optimal performance.

Fruit sampling for blueberry harvesting projections are labor-intensive and if not properly done cause delay and economic loss throughout the entire supply chain. The use of robots could be a promising tool to address these issues. However, field tests for robots in agricultural environments are not always readily available. This paper presents a simulation for a ROS based four-wheeled agricultural robot which navigates autonomously through blueberry bushes rows. The autonomous navigation system comprises a Simultaneous Localization and Mapping algorithm (LeGO-LOAM), a path planning algorithm (A*) and a path tracking algorithm (an ad hoc algorithm). Its performance is assessed using the ATE metric in a simulated blueberry farm environment in Gazebo simulator. The proposed navigation system allowed the robot to navigate successfully in this environment with the highest error reaching 500 mm and 250 mm for the x and y Euclidean components respectively. The presented simulation methodology will expedite development of agricultural robots and ultimately aid in overcoming the aforementioned issues.