Abstract
Inverse kinematics is one of the most important, most researched but still one among the most challenging problems in the robotics domain, for problems like motion generation and trajectory optimization. Various attempts have been made to propose a neural network that can solve the inverse kinematics problems. But not much emphasis has been given to analyze and compare its performance with other state-of-the-art methods. The major contribution of this paper is to present the performance analysis of one of the best neural networks proposed so far, and compare its results with the analytical approach. The main reason for using data-driven techniques like neural networks for inverse kinematics solution of robotic manipulators is that it can be extended to any number of links without much effort, while in other methods we have to consider number of links, types of links beforehand...
The work is funded by SERB, DST, Govt. of India to Dr. Vijay Bhaskar Semwal under the schema of Early career award, DST No: ECR/2018/000203 dated on 04/06/2019.
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This work is funded by SERB, DST, govt. of India for project under the schema of Early career award (ECR) with file no: ECR/2018/000203 dated on 04/06/2019. to Dr. Vijay Bhaskar Semwal.
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Semwal, V.B., Gupta, Y. (2022). Performance Analysis of Data-Driven Techniques for Solving Inverse Kinematics Problems. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_6
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