VISION-BASED GRASP PLANNING OF 3D OBJECTS USING GENETIC ALGORITHM
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Vision-based grasp planning can be approached as an optimization problem, where a hand configuration that indicates a stable grasp needs to be located in a large search space. In this thesis, we proposed applying genetic algorithm (GA) to grasp planning of 3D object in arbitrary shapes and any robot hand. Details are given on the selection of operators and parameters of GA. GraspIt! simulator  is used for implementing the proposed algorithm and as the test environment. A quantitative analysis including the comparison with simple random algorithm and simulated annealing (SA) method is carried out to evaluate the performance of the GA based planner. Both GA and SA grasp planner are tested on different sets of hand-object. And two different quality metrics are used in the planning. Given the same amount of time, GA is shown to be capable of finding a force-closure grasp with higher stability than SA.