A New Geometric Subproblem to Extend Solvability of Inverse Kinematics Based on Screw Theory for 6R Robot Manipulators

Leoro, Josuet; Hsiao, Tesheng; Betancourt, Carlos

doi:10.1007/s12555-019-0570-y

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…where f (•) is the forward kinematic function, while q is the vector of joint variables. Then, denoting the goal pose as p g , we construct a new function g(q) as g(q) = f (q) − p g (9) Let q 0 be the initial solution of the NN. Expanding g(q) with Taylor series at q 0 yields:…”

Section: Numerical Error Minimizationmentioning

confidence: 99%

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A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots

Zou

Jiang

2022

Sensors

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Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6—a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature.

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Section: Numerical Error Minimizationmentioning

confidence: 99%

“…For others, e.g., the general type robot, the closed-form solution does not exist. Numerous approaches based on different principles, which can be categorized as geometric [8,9], algebraic [10,11], and numerical methods [12][13][14], have been proposed. Unfortunately, all these methods suffer from inherent bottlenecks.…”

Section: Introductionmentioning

confidence: 99%