A Particle Swarm Optimization Algorithm for the Grasp Planning Problem

This paper discusses the use of intelligent technology to solve the problem of grasp planning known as a difficult problem. The scope aims to find points of contact between a five-fingered hand and an object. In this paper, we applied a new hierarchical approach for distributed Multi-Objective Particles Swarms Optimization, based on dynamic subdivision of the population using Pareto fronts (pbMOPSO) for the optimization of the grasp planning problem. The problem is based on simultaneous optimization of two objectives functions. The first objective is to explore the space of skillful manipulation of a robot hand with five fingers and find the best configuration of the fingers by minimizing the distance between the center of mass of the object and the center of the contact polyhedron. The second evaluation function is to maximize another quality measure that is related to the angles defining a configuration of the hand. An experimental study done with the HandGrasp simulator has shown a better performance of our algorithm to solve the grasp planning problem.

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A Multi-Objective Particle Swarm Optimization approach to robotic grasping

Walha

Bezine

Alimi

2013

2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR)

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Automatic grasp planning is an active field in robotic research. Its main purpose is to find the contact points between the robotic hand and an object in order to grasp it efficiently. As the robotic hand has many degrees of freedom which induce a huge number of solutions, the search for the "best" solution became an optimization problem. The search of such a solution is conducted by a grasp quality measurement which will be called the objective (or fitness) function. This paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO) approach to tackle the grasp planning problem. Its fitness functions are based in two different grasp quality measurements. The MOPSO approach is then tested in HandGrasp simulator with simple objects. The results will be compared with two simple Particle Swarm Optimization (PSO) approaches and demonstrate its performance.

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A Particle Swarm Optimization Algorithm for the Grasp Planning Problem

Cited by 3 publications

References 16 publications

An Adaptive Particle Swarm Optimization Method for Solving the Grasp Planning Problem

An Adaptive Particle Swarm Optimization Method for Solving the Grasp Planning Problem

Hierarchical design for distributed MOPSO using sub-swarms based on a population Pareto fronts analysis for the grasp planning problem

A Multi-Objective Particle Swarm Optimization approach to robotic grasping

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