Fuzzy Predictive Control Using Particle Swarm Optimization: Application to SCARA Robot

2014 ◽  
Vol 527 ◽  
pp. 230-236
Author(s):  
Mohamed Laid Hadjili ◽  
Kamel Kara ◽  
Oussama Ait Sahed ◽  
Jamal Bouyanzar
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Robot Arm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Compliant Assembly ◽  
Fuzzy Predictive Control

In this work a fuzzy model-based predictive control (FMPC) method that uses modified particle swarm optimization (PSO) is presented. The main objective of this work is the application of this method to the control of a Selective Compliant Assembly Robot Arm (SCARA) with four degrees of freedom (4-DOF).

scholarly journals Robot Arm Path Planning Using Modified Particle Swarm Optimization based on D* algorithm

2017 ◽  
Vol 13 (3) ◽  
pp. 27-37 ◽  
Author(s):  
Ahmed T. Sadiq ◽  
Firas A. Raheem
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Time Interval ◽  
Robot Arm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Cartesian Space ◽  
Quintic Polynomial

Abstract Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization algorithm is proposed to find an optimal path based on the local search, D* and Euclidean distances.  The quintic polynomial equation is utilized to provide a smooth trajectory path. According to the observe results, the modified PSO algorithm is efficiently performs to find an optimal path even in difficult environments.   Keywords: D*, Free Cartesian Space, Path Planning, Particle Swarm Optimization (PSO), Robot Arm.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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