Abstract
Redundant robotic systems offer flexibility during control and offer the advantage of achieving multiple tasks such as trajectory tracking, collision avoidance, joint limit avoidance, singularity avoidance, etc. However, it is difficult to achieve all these tasks without active prioritization as the environmental conditions can change and certain behaviors may be undesirable. This work proposes a fuzzy logic-aided inverse kinematics control technique that aims to actively prioritize these secondary tasks to ensure that minimal control effort is required for achieving all the tasks while operating the manipulator. The proposed control design leverages the advantages of fuzzy inference systems in order to properly assign weights to the secondary accelerations obtained using the null-space optimization technique. Through simulations, it is shown that the proposed controller is able to achieve all the secondary tasks while maintaining the primary tracking control.
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Chhabra, A., Karthikeyan, S., Choi, D., Kim, D. (2023). Fuzzy Logic-Aided Inverse Kinematics Control for Redundant Manipulators. In: Cohen, K., Ernest, N., Bede, B., Kreinovich, V. (eds) Fuzzy Information Processing 2023. NAFIPS 2023. Lecture Notes in Networks and Systems, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-031-46778-3_6
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DOI: https://doi.org/10.1007/978-3-031-46778-3_6
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