Intelligent sensing, planning, and control of a prototype robotic melon harvester is described. The robot consists of a Cartesian manipulator mounted on a mobile platform pulled by a tractor. Black and white image processing is used to detect and locate the melons. Incorporation of knowledge-based rules adapted to the specific melon variety reduces false detections. Task, motion and trajectory planning algorithms and their integration are described. The intelligent control system consists of a distributed blackboard system with autonomous modules for sensing, planning and control. Procedures for evaluating performance of the robot performing in an unstructured and changing environment are described. The robot was tested in the field on two different melon cultivars during two different seasons. Over 85% of the fruit were successfully detected and picked.

This paper presents a systems engineering method to evaluate the performance of an agricultural robot by simulating and comparing different types of robots, number of arms, multiple arm configurations, workspace design and dynamic characteristics. Numerical simulation tools were developed to quantify measures of machine performance such as cycle time and percentage of successful cycles based on an extensive statistical analysis using measured fruit locations and simulated crop parameters. The methodology developed was applied to determine design parameters for a robotic melon harvester. Simulation results indicated that the Cartesian robot was faster than the cylindrical robot for the melon harvesting task. Activating two arms in tandem was the fastest configuration evaluated.>