Srinivasu, Parvathaneni Naga and Bhoi, Akash Kumar and Jhaveri, Rutvij H. and Reddy, Gadekallu Thippa and Bilal, Muhammad (2021) Probabilistic Deep Q Network for real-time path planning in censorious robotic procedures using force sensors. Journal of Real-Time Image Processing, 18 (5). pp. 1773-1785. ISSN 1861-8200
Full text not available from this repository.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.