Particle swarm optimization for coconut detection in a coconut tree plucking robot

Alfin Junaedy, I. A. Sulistijono, Nofria Hanafi
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引用次数: 4

Abstract

High risk of climbing coconut tree manually become the main reason to build coconut tree plucking robot, not only the abnormality of bone but also the risk of falling from the coconut tree. The coconut tree plucking robot is made with the hope for helping people to pluck coconuts at the coconut tree easily and safely. Coconut tree with its condition make the coconuts difficult to be detected using image processing. Previous methods which are only work in indoor, only detect a coconut and only work on nearly uniform background are not suitable and easy to be disturbed with the interferences from the real condition in a coconut tree. An image processing with particle swarm optimization (PSO) method is introduced in this paper. It will find the best position of the coconuts at the tree and pluck it by giving a command to the arm to move toward the coconuts and cut its base by turning the grinder on the top of arm. Experiment results show that successful rate of the method to detect coconuts at the tree with cluttered background is 80% and then pluck them using the robot arm.
基于粒子群算法的椰树采摘机器人椰子检测
人工攀爬椰树的高风险成为构建拔椰树机器人的主要原因,除了骨骼异常,还有从椰树上掉下来的风险。摘椰子树机器人是为了帮助人们在椰子树上轻松安全地摘椰子而制造的。由于椰子树自身的状况,使得图像处理很难检测到椰子树。以往的方法只适用于室内,只检测椰子,只在几乎均匀的背景下工作,这些方法都不适合,而且容易受到真实情况的干扰。介绍了一种基于粒子群优化(PSO)的图像处理方法。它会找到椰子在树上的最佳位置,并通过向手臂发出命令来采摘椰子,并通过转动手臂顶部的研磨机来切割椰子底座。实验结果表明,该方法在背景杂乱的树上检测椰子,并用机械臂采摘的成功率为80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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