Analysis of a motion planning problem for sweet-pepper harvesting in a dense obstacle environment. (June 2016)

• Record Type: Journal Article
• Authors: Bac, C. Wouter; Roorda, Tim; Reshef, Roi; Berman, Sigal; Hemming, Jochen; van Henten, Eldert J.
• Is Part Of: Biosystems engineering. Volume 146(2016:Jun.)
• Page Start: 85
• Page End: 97
• Publication Date: 2016-06
• Subjects: Motion planning -- Agricultural robot -- Sensitivity analysis -- Rapidly exploring random trees -- Grasp pose; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2015.07.004
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : To reach a fruit in an obstacle-dense crop environment, robotic fruit harvesting requires a collision-free motion of the manipulator and end-effector. A novel two-part analysis was conducted of a sweet-pepper harvesting robot based on data of fruit (N = 158) and stem locations collected from a greenhouse. The first part of the analysis compared two methods of selecting the azimuth angle of the end-effector. The new 'constrained-azimuth' method avoided risky paths and achieved a motion planning success similar to the 'full-azimuth' method. In the second part, a sensitivity analysis was conducted for five parameters specifying the crop (stem spacing and fruit location), the robot (end-effector dimensions and robot position) and the planning algorithm, to evaluate their effect on successfully finding a collision-free goal configuration and path. Reducing end-effector dimensions and widening stem spacing are promising research directions because they significantly improved goal configuration success, from 63% to 84%. However, the fruit location at the stem is the strongest influencing parameter and therefore provides an incentive to train or breed plants that develop more fruit at the front side of the plant stem. The two analyses may serve as useful tools to study motion planning problems in a dense obstacle environment. Highlights: Twofold analysis of motion planning success in a dense obstacle environment. Novel constrained-azimuth method avoided executing risky paths. Sensitivity analysis for parameters specifying crop, robot, and planning algorithm. Reducing end-effector dimensions and widening stem spacing improve success. Fruit location at the stem is the strongest influencing parameter.