Solution of inverse kinematics problems of a highly kinematically redundant manipulator using genetic algorithms

Buckley, K.A.; Hopkins, S. H.; Turton, Brian

doi:10.1049/cp:19971191

Cited by 9 publications

(6 citation statements)

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“…Offset modification method presented to solve inverse Kinematics problem for serial robot manipulator with any DOFs, it was shown that this method is suitable for real time manipulator control [14]. Genetic algorithm widely used for determining inverse position, velocity and acceleration [15], [16], [17] and [1]. The combination of using genetic algorithm and neural network also used for inverse positioning of robots manipulators, GA and ANN complete each other to find the final inverse position [18] and [19].…”

Section: Reviewmentioning

confidence: 99%

Inverse Acceleration Solution for Robot Manipulators using Harmony Search Algorithm

Ghafil¹

2016

IJCA

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Inverse acceleration problem is very difficult for a serial robot having less than 6 degree of freedom (DOFs), this difficulty is due to the complexity of the inverse Jacobian matrix. For the sake of this problem an approach to solve inverse acceleration of a robot was introduced in this paper, in which harmony search algorithm (HSA) was used to calculate the inverse problem without calculating inverse Jacobian matrix. It is proved that it is applicable by simulation inverse acceleration for a 3-DOF robot. ANSYS 15.0 was used as a simulation software package.

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Section: Reviewmentioning

confidence: 99%

Inverse Acceleration Solution for Robot Manipulators using Harmony Search Algorithm

Ghafil¹

2016

IJCA

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“…Originally, J. Parker et al were the first to propose a binary genetic algorithm to solve the inverse kinematics of a redundant 4-DOF robot manipulator as the minimization of the end-effector position error and the joint maximum displacement [11]. Likewise, in [17], [18] a similar approach was used to solve the inverse kinematics of highly redundant robot manipulators with obstacle avoidance. Nonetheless, binary coding of real parameters introduces accuracy errors not suitable for complex manipulation tasks.…”

Section: Related Workmentioning

confidence: 99%

A memetic approach to the inverse kinematics problem

González

Blanco

Moreno

2012

2012 IEEE International Conference on Mechatronics and Automation

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Solving the inverse kinematics problem is at the core of the kinematics control of any articulated mechanism. It refers to determine the joint configuration that places the end-effector in an arbitrary position and orientation in the workspace. Kinematic inversion algorithms are generally based on the (pseudo)inverse Jacobian matrix, however, these methods are local and unstable in the vicinity of singular joint configurations. Alternatively, the inverse kinematics can be formulated as a constrained minimization problem in the robot configuration space. In a previous work, Differential Evolution (DE) was used to solve this optimization problem for a non-redundant robot manipulator. Although, the algorithm was successful in finding accurate solutions it showed a low convergence speed rate. In this paper, a memetic approach is proposed to increase the convergence speed of the DE by introducing a local search mechanism, called discarding. The proposed approach is tested in a simulation environment to solve the kinematic inversion problem of a non-redundant 3DOF robot manipulator. Experimental results shows that the proposed algorithm is able to find solutions with high accuracy in less generations than the original DE approach.

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“…In heuristic based IK solver we can identify two main classes: Computational based techniques and learning based techniques. The first class is based on nature inspired heuristics such as Particle Swarm Optimization, PSO Xu and Li, 2009;, Ant Colony Optimization, ACO (Mohamad et al, 2006), Genetic Algorithms, GA (Edison and Shima, 2011;Zhang and Nelson, 2011;Buckley et al, 1997) or Ant Bee Colony, ABC (Cavad et al, 2013). For these methods, the IK problem is addressed as an optimization and the heuristics is used as computing alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…In inverse kinematics based on PSO Alimi, 2013, 2012); or on GA such as (Edison and Shima, 2011;Zhang and Nelson, 2011;Buckley et al, 1997), a stochastic search is performed, using a population in GA or a set of individuals in PSO, ABC and ACO; each population or individual is a possible solution, here a set of joints positions of the IK problem. Any possible solution is ranked, using a fitness function and the best is returned as the solution of the problem of a given input, here a target position.…”

Section: Introductionmentioning

confidence: 99%

IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm

Rokbani

Casals

Alimi

2014

Studies in Computational Intelligence

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In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics, the proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of jointpositions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 2 links and 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (α, β, γ, δ) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about (2.3394 3 10 − ) seconds with a position error around (3.116 8 10 − ) for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 9 10 − .

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Solution of inverse kinematics problems of a highly kinematically redundant manipulator using genetic algorithms

Cited by 9 publications

References 0 publications

Inverse Acceleration Solution for Robot Manipulators using Harmony Search Algorithm

Inverse Acceleration Solution for Robot Manipulators using Harmony Search Algorithm

A memetic approach to the inverse kinematics problem

IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm

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