Home /Research /Particle swarm optimization for coconut detection in a coconut tree plucking robot
SWARM

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

Alfin Junaedy, Indra Adji Sulistijono, Nofria Hanafi

Year
2017
Citations
10

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.

Keywords

Tree (set theory)Particle swarm optimizationComputer scienceRobotArtificial intelligenceComputer visionMathematicsMachine learning

Related papers

Browse all SWARM papers