A Two‐Stage Recognition and Planning Approach for Grasping Manipulators and Design of End‐Effectors

ABSTRACT The development of selective harvesting robotics in agriculture and horticulture has gained a lot of attention and momentum in recent years, but commercial products are still lacking. This paper develops a sweet pepper harvesting robot and focuses on sweet pepper peduncle detection, the design of the end‐effector, and the motion planning of the manipulator to improve the harvesting success rate. To address the low recognition accuracy of sweet pepper peduncles, a detection algorithm designed explicitly for sweet pepper peduncles is proposed by storing the observation position in advance, which involves dividing the recognition process into two steps. The recognition success rate of picking points is 94.4%. To address the problem of low efficiency and low success rate of harvesting robots, a two‐stage motion planning (TSMP) method is proposed to divide the whole picking process into offline and online planning. Offline planning adopts a database to plan trajectories to pre‐stored observation points to ensure that the manipulator identification of fruit peduncles at close range is with solutions and the trajectory is reliable, while online planning proposes an adaptive end‐effector grasping pose control algorithm and analyzes and calculates the best grasping pose for the manipulator based on the sweet pepper peduncles pose. A novel sweet pepper end‐effector is designed and integrated into a fully automatic harvesting robot system. Finally, the proposed method is experimentally verified on the developed sweet pepper harvesting robot. The field experiment results demonstrate that the robot can continuously harvest sweet peppers with a harvesting success rate of 77.16%. The average picking time is about 15 s with a fruit recovery device. Supplementary video is available at https://www.youtube.com/watch?v=vnlRTkjPD2U .