Towards a Particle Swarm Optimization approach for grasp planning problem

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

doi:10.1109/biorob.2012.6290672

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…We compared the results with using two simple PSO algorithms. PSO1 an respectively on fitness function 1 and f [23] [24]. For the first test, we run the grasp p grip a sphere with a diameter 5 cm.…”

Section: Simulation Resultsmentioning

confidence: 99%

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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Section: Simulation Resultsmentioning

confidence: 99%

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)

Self Cite

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“…PSO techniques are implemented to find out the most suitable grasp planning for a multi-fingered robotic hand considering it as an optimization problem. 69,70 The authors examined the kinematics of the hand and simulated the proposed method in the HandGrasp simulator. Grasp planning of the multi-fingered robot hand is obtained by adaptive PSO techniques.…”

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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Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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Towards a Particle Swarm Optimization approach for grasp planning problem

Cited by 4 publications

References 27 publications

A Multi-Objective Particle Swarm Optimization approach to robotic grasping

A Multi-Objective Particle Swarm Optimization approach to robotic grasping

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

Overview of evolutionary algorithms and neural networks for modern mobile communication

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