Continuous picking of yellow peaches with recognition and collision-free path

Abstract

In recent years, picking robots can be optimized through deep algorithms and path planning. However, the traditional picking robot grabbing work method has a long grabbing cycle. This paper proposes a continuous picking scheme for yellow peaches. First, we proposed a comparison between the calculation method of the continuous picking method and the traditional picking method. It provided a yellow peach-picking robot and a continuous picking end effector. We designed and improved the YOLOv5s model, adding model clipping, ODconv, Global Context Networks and ELAN methods. The best model can be obtained through multiple ablation experiments. We proposed a collision-free continuous picking path (CPP) by Volume variables and a three-dimensional Gaussian kernel. It can be applied to the yellow peach continuous-picking robot. After the improved YOLOv5s network, the MAP and model size reached mAP, and the model size reached 96 % and 3.54 MB. Compared with the original and improved networks, the improved network parameters were reduced by 75.5 %, and the mAP was increased by 4.6 %, reducing the deployment cost. In the simulation experiment, the non-collision continuous picking algorithm was compared with the planning method under the traditional picking method. The continuous picking path is 29.56 % of the original path, the theoretical non-collision rate reaches 96.6 %, the actual non-collision rate is 91.6 %, and the picking success rate is 80 %. The scheme can realize continuous picking of yellow peaches. This scheme provides an essential reference for the continuous picking of fruits.