Autonomous Navigation of a Four-Wheeled Robot in a Simulated Blueberry Farm Environment

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.