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.

No abstract

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.

This paper addresses the grasp planning problem, which deals with finding the contact points between a fivefingered hand and an arbitrary object. As we consider this problem as an optimization problem, we provide in this paper an approach based on Particle Swarm Optimization for the generation and execution of grasps. Its main purpose is to compute a set of hand configurations posture in order to find an appropriate grip, satisfying a certain criteria. Assuming that the search for a solution is restricted to a precise grasp which allows only contact with the fingertips, we analyze each of the configurations of the hand with a fitness function based on a measure of quality of the grasp. Each grasp is tested and evaluated within our grasping simulator "HandGrasp". We present experimental results using different objects.

Computing a set of contact points between a robotic hand and an object in order to fulfill some criteria is the main problem of the grasp planning. An automatic grasp planning can produce a set of joint angles defining a configuration of the robotic hand. The huge number of solutions that satisfy a good grasp is the main difficulty of such a planner. In this paper, we represent the grasp planning problem as an optimization problem and we propose a new algorithm based on a Particle Swarm Optimization (PSO) technique. To generate the positions of the fingertips, the kinematic of the hand is modeled. Therefore, a simple PSO algorithm is described to optimize the workspace of the operating hand based on a quality of measure of the grasp. The simulation results support the effectiveness of our approach.