Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil, Fatin Hassan; Ibraheem, Ibraheem Kasim; Sahib, Mouayad A.; Humaidi, Amjad J.

doi:10.1016/j.asoc.2020.106076

Cited by 142 publications

(87 citation statements)

References 38 publications

(52 reference statements)

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“…An improved version of the genetic algorithm (GA), based on special selection and the crossover function, led to a reduced computation time of GA. In addition to the shortest global path in hexagonal grid modelling, which was investigated in Reference [6], the shortest and smoothest safest path in static and dynamic environment was obtained using the Hybrid PSO-MFB algorithm and a local search, in addition to the obstacle detection and avoidance (ODA) technique, as presented in Reference [7]. Researchers in Reference [8] developed an ant colony optimization (ACO) path planner by improving the probability of selecting the optimal path to establish target attraction and proposed a wolf colony to update pheromones for an explosion proof robot (EPR).…”

Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

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131

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Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

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Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

Self Cite

131

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“…Although the collision path can be moved to the feasible region depending on the searching ability of the algorithm itself, it requires many iterations of the algorithm and is a slow process. Therefore, some methods [26,27] are proposed to move the collision path to the feasible region. All of these processing methods speed up the convergence of the algorithm by moving the collision path to the outside of the obstacle in one operation, and also reduce the infeasible paths in the population rapidly.…”

Section: B the Treatment Of Infeasible Pathmentioning

confidence: 99%

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

Fan

Hou

2020

IEEE Access

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In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with selfadaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time. INDEX TERMS path planning, improved firefly algorithm, self-adaptive population size.

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“…This optimization tuner is characterized by fast convergence, the efficiency of computation and it has the capability to find local and global solutions [ 31 , 32 ]. Other modern and generalized optimization techniques can be employed either to improve the optimization process or to make a comparison in performance among each other [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Humaidi

Ibraheem

Azar

et al. 2020

Entropy

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

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Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Cited by 142 publications

References 38 publications

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

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