A Multi-Objective Particle Swarm Optimization approach to robotic grasping

Walha, Chiraz; Bezine, Hala; Alimi, Adel M.

doi:10.1109/icbr.2013.6729267

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…It involves the derivation of mathematical model, followed by simulation of the derived mathematical model of the multi-fingered hand which is influenced only by the effect of gravity, development of tracking control algorithm, simulation of the control law and finally visualizing in 3D space the simulation results with a 3D grasping simulator developed in our laboratory REGIM [8]. In the following sections, we will explain these steps.…”

Section: Methodsmentioning

confidence: 99%

Lagrange Dynamic Modeling of a Multi-Fingered Robot Hand in Free Motion Considering the Coupling Dynamics

Boughdiri

Nasser

Bezine

et al. 2012

AMR

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Multi-fingered robot hands have been one of the major research topics because several robotic systems, including service robots, industrial robots and wheel-type mobile robots require grasping and manipulation of a variety of objects as crucial functionalities. Roughly speaking, there are two different types of robotic behavior: free motion, purpose of this paper and constrained motion that would be published in the near future. In this paper, we address the problem of multi-fingered robot hand’s dynamic modeling which is fundamental in design of model-based controllers for grasping and manipulation tasks. Based on the specified multi-fingered robot hand, a new methodology for deriving an efficient dynamic equation by the Lagrange formulation is presented. This methodology is new in the sense that it considers the coupling dynamics of the system in the identification of the parameters of the dynamic equation. Furthermore the developed dynamic model leads to decoupling dynamic characteristics, by which the control of different parts of the system can be separately simulated. So the new structure of the dynamic model was very useful and effective for the simulation and the diagnostic. Several simulation results proved that the derived dynamic model can predict the motion of the multi-fingered hand in free motion.

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Section: Methodsmentioning

confidence: 99%

Lagrange Dynamic Modeling of a Multi-Fingered Robot Hand in Free Motion Considering the Coupling Dynamics

Boughdiri

Nasser

Bezine

et al. 2012

AMR

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show abstract

“…In order to benchmark the performance of the proposed method compared to existing multi-objective approaches used for inverse kinematics, MO-m-PSO was compared to multi-objective PSO for inverse kinematics, MO-PSO, and in [36], multi-objective CSA [37] and multi-objective JAYA algorithm (MO-JAYA) [38]. Comparatives are based on 30 random points RUNS and using the Wilcoxon nonparametric method.…”

Section: Multi-objective M-pso Mo-m-pso Comparative Analysismentioning

confidence: 99%

A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm

et al. 2022

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In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model.

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“…Grasp planning of the multi-fingered robot hand is obtained by adaptive PSO techniques. 71 Multiobjective PSO (MOPSO) 72 and distributed MOPSO based on the dynamic subdivision of the population using Pareto fronts 73 is implemented to find out the optimal grasp planning in a five-fingered hand in HandGrasp simulator using two different grasp quality parameters. Grasp quality improvement in robotic grasp planning is carried out using PSO.…”

Section: Application Of Soft Computing Techniques In Robotic Graspingmentioning

confidence: 99%

Application of soft computing methods in robotic grasping: A state-of-the-art survey

Mahanta

Deepak²,

Biswal³

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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Robotic grasping has become one of the most important domains of robotics research over the past few decades due to its wide range of applications in industrial automation. The model of grasping objects by robot hand depends on a good number of factors, such as type and size of the object, the morphology of object, type of hand, degree of freedom, etc. Thus, the model sometimes becomes mathematically intractable. With the progress in computational capability, soft computing methods have found a way to address the challenges faced by traditional methods while dealing with the robotic grasping problem. This article aims to summarize the outcome of a systematic study in the field of application of soft computing methods in robotic grasping and manipulation. The key processes of robotic grasping where soft computing methods are applied are identified, and research contributions of all processes are analyzed. This review presents a state-of-the-art survey and attempts to find the research gaps in the area of soft computing applications to address the robotic grasping problem.

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A Multi-Objective Particle Swarm Optimization approach to robotic grasping

Cited by 5 publications

References 20 publications

Lagrange Dynamic Modeling of a Multi-Fingered Robot Hand in Free Motion Considering the Coupling Dynamics

Lagrange Dynamic Modeling of a Multi-Fingered Robot Hand in Free Motion Considering the Coupling Dynamics

A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm

Application of soft computing methods in robotic grasping: A state-of-the-art survey

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