Abstract
To solve the problems of unclear boundaries and inconsistent influence weights among prioritization evaluation factors for grasping stacked fruit clusters by parallel robots, a fuzzy comprehensive evaluation method for the grasping prioritization of stacked fruit clusters based on a relative hierarchy factor set is proposed. According to the morphological features of stacked fruit clusters and motion features of parallel robots, a hierarchical tree model without a cross based on a subtree structure is constructed to analyze the multiple factors with unclear boundaries. A relative factor set with positive and negative effects is constructed, and a mathematical expectation is used to construct an average random consistency index and consistency satisfaction value for improving the consistency of influence weights and precision of consistency verification for a comparison matrix. The weight vector is constructed from the top to the bottom of the model, and the membership matrix of the multi-layer factors and grasping prioritization are calculated from bottom to top. The results showed that the average precision of grasping prioritization of stacked fruit clusters based on the proposed method increased by 27.77% compared with the existing fuzzy comprehensive evaluation method. The proposed method can effectively improve prioritization precision for grasping randomly stacked fruit clusters affected by multiple factors and can further realize accurate automatic sorting.
Highlights
The automatic sorting of fruits by robots is of great significance in the automated and intelligent development of agricultural production and agriculture product processing [1,2]
The membership matrix and comprehensive evaluation value of the multi-layer factors are calculated from the bottom to the top of the model to evaluate the grasping prioritization of stacked fruit clusters
This paper proposes a fuzzy comprehensive evaluation method for the grasping prioritization of stacked fruit clusters based on the relative hierarchy factor set to further improve grasping success of stacked fruit clusters by low-DOF parallel robots
Summary
The automatic sorting of fruits by robots is of great significance in the automated and intelligent development of agricultural production and agriculture product processing [1,2]. During automatic sorting of fruits, accurate and reliable grasping detection is a precondition for achieving accurate, fast and nondestructive robotic manipulation [4]. Because six degrees of freedom are not needed in the automatic sorting of fruits, the use of a 6−DOF (degree of freedom) robot will increase the unnecessary cost [10]. Compared with a 6−DOF robot, a low-DOF parallel robot with less than six degrees of freedom is more suitable for fruit automatic sorting because of its low cost, high accuracy and fast speed, but these features place greater demand on grasping prioritization [11]. The cluster features, stalk features, grasping position constraints and change difficulties of the robot pose are considered as evaluation factors for grasping prioritization of stacked fruit clusters. Due to the unclear boundaries among evaluation factors and the different grasping prioritization weights, it is difficult to make a precise comprehensive evaluation for grasping prioritization of stacked fruit clusters in multiple evaluation factors
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