Robotic Aubergine Harvesting Using Dual-Arm Manipulation

Abstract

Interest in agricultural automation has increased considerably in recent decades due to benefits such as improving productivity or reducing the labor force. However, there are some current problems associated with unstructured environments make developing a robotic harvester a challenge. This article presents a dual-arm aubergine harvesting robot consisting of two robotic arms configured in an anthropomorphic manner to optimize the dual workspace. To detect and locate the aubergines automatically, we implemented an algorithm based on a support vector machine (SVM) classifier and designed a planning algorithm for scheduling efficient fruit harvesting that coordinates the two arms throughout the harvesting process. Finally, we propose a novel algorithm for dealing with occlusions using the capabilities of the dual-arm robot for coordinate work. Therefore, the main contribution of this study is the implementation and validation of a dual-arm harvesting robot with planning and control algorithms, which, depending on the locations of the fruits and the configuration of the arms, enables the following: (i) the simultaneous harvesting of two aubergines; (ii) the harvesting of a single aubergine with a single arm; or (iii) a collaborative behavior between the arms to solve occlusions. This cooperative operation mimics complex human harvesting motions such as using one arm to push leaves aside while the other arm picks the fruit. The performance of the proposed harvester is evaluated through laboratory tests that simulate the most common real-world scenarios. The results show that the robotic harvester has a success rate of 91.67% and an average cycle time of 26 s/fruit.

Keywords

• Aubergines
• dual-arm robot manipulation
• eggplants
• intelligent perception
• machine vision
• occlusions