A conceptual framework and optimization for a task-based modular harvesting manipulator

This paper provides a new concept of a modular, task-based agricultural robot whose construction can be changed using different combinations of basic parts. This way, the robot structure can be optimized to different tasks. This kind of robot combines the advantages of economical task-based robots with the ability to multitask, typically provided by costly universal robots. The modular approach can increase the profitability of agriculture robot manipulators and opens the door for robotic harvesters into agriculture. We present the entire flow of designing this type of modular agricultural manipulator. We first measure and model real trees and then calculate the time it takes the robot to reach every fruit according to the robot structure and location. Lastly, we use our optimization approach to find a robot optimal for pre-defined tasks, such as maximizing the number of fruit harvested or minimizing the picking time. Simulation results, based on actual three-dimensional tree models, show that modular robots can collect 30-40 percent more fruit than a robot with a fixed structure of the same complexity. Furthermore, a modular robot can be adapted to every season for different types of fruit; consequently, it provides much better results both in harvesting time and in amount of harvested fruit. Thus, a modular robot has the potential to increase the utilization compared with a fixed structure robot and helps overcome low-utilization barrier of entrance of robots into agriculture.