A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm

Elsherbiny, Ahmed; Elhosseini, Mostafa A.; Haikal, Amira Y.

doi:10.1016/j.asoc.2018.08.028

Cited by 41 publications

(20 citation statements)

References 20 publications

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“…In the SDE algorithm, it was considered to use the amplification factor F = 0.5 and crossover constant C R = 0.8 based on Fan, Xie & Zhou (2019). The particular parameter setting for KABC are the forager bees population P f = 20, the onlooker bees population P o = N − P f , and stagnation limit parameter L = (N * D)/2, see Karaboga & Basturk (2007) and El-Sherbiny, Elhosseini & Haikal (2018). With respect to the common parameter settings, it was considered to use a population size of N = 30 individuals, a total of G max = 1,000 generations, and a tolerance of s stop = 1 × 10 −4 .…”

Section: Resultsmentioning

confidence: 99%

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Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

Hernández-Barragán¹,

López-Franco²,

Arana‐Daniel³

et al. 2021

PeerJ Computer Science

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This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

“…In El-Sherbiny, Elhosseini & Haikal (2018), a new variant of ABC is introduced to solve the inverse kinematics of robot manipulators, which is called KABC. The reported results prove that the KABC algorithm performed better than the classical ABC.…”

Section: Introductionmentioning

confidence: 99%

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

Hernández-Barragán¹,

López-Franco²,

Arana‐Daniel³

et al. 2021

PeerJ Computer Science

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show abstract

“…The performances of the proposed algorithm and that of the competitive algorithms are evaluated through two groups of benchmark functions; 18 scalable functions taken from [27] and 30 complex functions taken from CEC'2017 [50]. The definition of the functions in the first group and their global optima are listed in Table 1; f1 -f10 are the uni-modal functions, and f11 -f18 are the multi-modal functions.…”

Section: The Experiments and Evaluationsmentioning

confidence: 99%

A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

Apinantanakon¹,

Sunat²,

Chiewchanwattana³

2018

Preprint

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A swarm based nature-inspired optimization algorithm namely fruit fly optimization algorithm (FOA) has simple structure and ease of implementation. However, FOA has a low success rate and a slow convergence because FOA generates new positions around the best location using fixed search radius. Several improved FOAs have been proposed. But their exploration ability is questionable. To make the search process to transit from the exploration phase to the exploitation phase smoothly, this paper proposes a new FOA constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). It has two population types working together. CPFOA's performance is evaluated by 18 well-known standard benchmark, and 30 CEC’2017 functions. The results showed that CPFOA outperforms both the original FOA and its variants in terms of convergence speed and performance accuracy. The results base on CEC’2017 show that CPFOA can achieve a very promising accuracy when compared with the well-known competitive algorithms. CPFOA is applied to optimize two applications; the MLPs classifying real datasets and extracting parameters of T-S fuzzy system for modelling Box and Jenkins gas furnace data set. CPFOA can find parameters having a very high quality compared with the best known competitive algorithms.

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“…So far, various optimization algorithms such as genetic algorithm (GA) [6,7], particle swarm optimization algorithm (PSO) [8][9][10][11][12], differential evolution algorithm (DE) [2,13,14], artificial bee colony algorithm (ABC) [15], and biogeographybased optimization [16] have been proposed to calculate the inverse kinematics solutions of robots; thus the shortcomings of the conventional methods are effectively overcome. In this study, a fitness function was constructed to minimize the pose error of end-effector.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Mutation Fruit Fly Optimization Algorithm for Solving the Inverse Kinematics of a Redundant Robot Manipulator

Shi

Mao

et al. 2020

Mathematical Problems in Engineering

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The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics. Simultaneously, it is also the basis for motion control, trajectory planning, and dynamics analysis of redundant manipulators. Taking the minimum pose error of the end-effector as the optimization objective, a fitness function was constructed. Thus, the inverse kinematics problem of the redundant manipulator can be transformed into an equivalent optimization problem, and it can be solved using a swarm intelligence optimization algorithm. Therefore, an improved fruit fly optimization algorithm, namely, the hybrid mutation fruit fly optimization algorithm (HMFOA), was presented in this work for solving the inverse kinematics of a redundant robot manipulator. An olfactory search based on multiple mutation strategies and a visual search based on the dynamic real-time updates were adopted in HMFOA. The former has a good balance between exploration and exploitation, which can effectively solve the premature convergence problem of the fruit fly optimization algorithm (FOA). The latter makes full use of the successful search experience of each fruit fly and can improve the convergence speed of the algorithm. The feasibility and effectiveness of HMFOA were verified by using 8 benchmark functions. Finally, the HMFOA was tested on a 7-degree-of-freedom (7-DOF) manipulator. Then the results were compared with other algorithms such as FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA. The pose error of end-effector corresponding to the optimal inverse solution of HMFOA is 10−14 mm, while the pose errors obtained by FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA are 102 mm, 10−1 mm, 10−2 mm, 102 mm, and 102 mm, respectively. The experimental results show that HMFOA can be used to solve the inverse kinematics problem of redundant manipulators effectively.

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A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm

Cited by 41 publications

References 20 publications

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

Hybrid Mutation Fruit Fly Optimization Algorithm for Solving the Inverse Kinematics of a Redundant Robot Manipulator

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