Learning the inverse kinematics of a robot manipulator using the Bees Algorithm

Pham, Duc Truong; Fahmy, Ashraf

doi:10.1109/indin.2008.4618151

Cited by 59 publications

(32 citation statements)

References 8 publications

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“…To improve the search accuracy and computation performance, two other procedures are introduced in [51,52]. In the first approach, the search begins within the initial neighbourhood size of sites (nh) of the basic BA.…”

Section: Previous Modifications To the Bees Algorithmmentioning

confidence: 99%

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Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm

2017

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Abstract:In many non-deterministic search algorithms, particularly those analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. The BA is a population-based metaheuristic search algorithm inspired by bees seeking nectar/pollen. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 well-known benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much more quickly.

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Section: Previous Modifications To the Bees Algorithmmentioning

confidence: 99%

“…Therefore, the patch exploration is stopped and a new random patch is generated for hunting. This process is known as "abandoning sites without new information" [52] or "site abandonment" [34,52].…”

Section: Previous Modifications To the Bees Algorithmmentioning

confidence: 99%

Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm

2017

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“…[8] proposed inverse kinematic solution on the basis of quaternion transformation. [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] have proposed application of quaternion algebra for the solution of inverse kinematics problem of different configurations of robot manipulator. [35] presented a quaternion method for the demonstrating kinematics and dynamics of rigid multibody systems.…”

Section: Introductionmentioning

confidence: 99%

“…[21] proposed inverse kinematic solution for 6R revolute manipulator using real time optimization algorithm. [24] proposed kinematic solution using three different methods such as bee algorithm, neural network which is later optimized by bee algorithm and evolutionary algorithm. [2] proposed kinematic solution of 3-dof serial robot manipulator using real time genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization Approach for Inverse Kinematic Solution

Jha¹,

Biswal²

2017

Kinematics

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Inverse kinematics of serial or parallel manipulators can be computed from given Cartesian position and orientation of end effector and reverse of this would yield forward kinematics. Which is nothing but finding out end effector coordinates and angles from given joint angles. Forward kinematics of serial manipulators gives exact solution while inverse kinematics yields number of solutions. The complexity of inverse kinematic solution arises with the increment of degrees of freedom. Therefore it would be desired to adopt optimization techniques. Although the optimization techniques gives number of solution for inverse kinematics problem but it converses the best solution for the minimum function value. The selection of suitable optimization method will provides the global optimization solution, therefore, in this paper proposes quaternion derivation for 5R manipulator inverse kinematic solution which is later compared with teachers learner based optimization (TLBO) and genetic algorithm (GA) for the optimum convergence rate of inverse kinematic solution. An investigation has been made on the accuracies of adopted techniques and total computational time for inverse kinematic evaluations. It is found that TLBO is performing better as compared GA on the basis of fitness function and quaternion algebra gives better computational cost.

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“…In the recent years, the robot control problem has received considerable attention due to its complexity. Inverse kinematics modeling has been one of the main problems in robotics research, there has been a lot of research on the use of neural networks for control The most popular method for controlling robotic arms [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%