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

机译:椰子树中椰子树挖掘机机器人的粒子群优化

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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.
机译:攀爬椰子树的高风险手动成为打造椰子树采摘机器人的主要原因,不仅是骨骼的异常,而且落下椰子树的风险。采用机器人的椰子树是用帮助人们轻松而安全地在椰子树上拍椰子的希望。椰子树及其条件使得使用图像处理难以检测椰子。以前的方法仅在室内工作,只能检测椰子,并且仅在近均匀的背景上工作并不适合,并且易于与椰子树中真实情况的干扰扰乱。本文介绍了具有粒子群优化(PSO)方法的图像处理。它会发现在树上的椰子的最佳位置,并通过发出指令,以手臂朝椰子移动和手臂上方转动粉碎机削减基地采摘它。实验结果表明,使用杂乱的背景检测树木的椰子的方法的成功率为80 %,然后使用机器人臂采摘它们。

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