Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors | Journal of Real-Time Image Processing
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Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors

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Abstract

In recent years, enormous advancement has taken place in biomedical engineering, which has paved the way for robot-assisted surgery in various complex surgical procedures. In robotic surgery, the reinforcement-based Temporal Difference (TD) based approach through assistive approaches has tremendous potential. Probabilistic Roadmap (PR) can be used for recognition of the path to the region of interest without any obstacles and, Inverse Kinematics (IK) approach can be used for the accurate approximation of the pixel space to the real-time workspace. Our proposed system would be more effective in approximating the path length, depth evaluation, and less invasive contact force sensor. This article presents a robust algorithm that would assist in robotic surgery for censorious surgeries in real-time. For working on such soft tissues, software-driven procedures and algorithms must be more precise in choosing the optimal path for reaching out to the procedural region. The statistical analysis has proven that the proposed approach would be outperforming under favorable learning rate, discount factor, and the exploration factor.

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Acknowledgements

We thank the anonymous referees for their useful suggestions in improvising the paper

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Authors and Affiliations

  1. Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, 530045, India

    Parvathaneni Naga Srinivasu

  2. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, 737136, India

    Akash Kumar Bhoi

  3. Department of Computer Science and Engineering, PanditDeendayal Petroleum University, Gandhinagar, India

    Rutvij H. Jhaveri

  4. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Gadekallu Thippa Reddy

  5. Department of Computer Engineering, Hankuk University of Foreign Studies, Yongin-si, 17035, South Korea

    Muhammad Bilal

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  1. Parvathaneni Naga Srinivasu
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Correspondence to Muhammad Bilal.

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Srinivasu, P.N., Bhoi, A.K., Jhaveri, R.H. et al. Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors. J Real-Time Image Proc 18, 1773–1785 (2021). https://doi.org/10.1007/s11554-021-01122-x

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