Real-Time Adaptive Control of a Flexible Manipulator Using Reinforcement Learning

Pradhan, Santanu Kumar; Subudhi, Bidyadhar

doi:10.1109/tase.2012.2189004

Cited by 89 publications

(33 citation statements)

References 11 publications

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“…One of the main challenges for this scheme is online feedback during the compensation that is hard to achieve, especially in surgical robotic systems where sterilization and miniature size of flexible tools are necessary. [319,320] 7) "Neural Networks (NN)based control" is a technique used when dealing with unknown system dynamics. Users can eliminate the use of complex mathematical models from the system.…”

Section: Advanced Control Algorithm For Surgical Robotsmentioning

confidence: 99%

Advanced Intelligent Systems for Surgical Robotics

Thai

Phan

Hoang

et al. 2020

Advanced Intelligent Systems

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Surgical robots have had clinical use since the mid‐1990s. Robot‐assisted surgeries offer many benefits over the conventional approach including lower risk of infection and blood loss, shorter recovery, and an overall safer procedure for patients. The past few decades have shown many emerging surgical robotic platforms that can work in complex and confined channels of the internal human organs and improve the cognitive and physical skills of the surgeons during the operation. Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. Despite advances, current surgical intervention systems are not able to execute autonomous tasks and make cognitive decisions that are analogous to those of humans. Herein, the historical development of surgery from conventional open to robotic‐assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems is reviewed. Also, available autonomous surgical platforms are comprehensively discussed with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic‐assisted surgical systems toward the achievement of fully autonomous operation.

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Section: Advanced Control Algorithm For Surgical Robotsmentioning

confidence: 99%

Advanced Intelligent Systems for Surgical Robotics

Thai

Phan

Hoang

et al. 2020

Advanced Intelligent Systems

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show abstract

“…More advanced robust adaptive control uses a weighted bank of extended Kalman filters as a mixture-of-experts [218]. Reinforcement learning may also be used to implement adaptive controllers to respond to changes in payload [219]. SSRMS used fixed control gains rather than adaptive gains due to the latter's computational complexity.…”

Section: Freeflyer Manipulator Control Systemsmentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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Space-based manipulators have traditionally been tasked with robotic on-orbit servicing or assembly functions, but active debris removal has become a more urgent application. We present a much-needed tutorial review of many of the robotics aspects of active debris removal informed by activities in on-orbit servicing. We begin with a cursory review of on-orbit servicing manipulators followed by a short review on the space debris problem. Following brief consideration of the time delay problems in teleoperation, the meat of the paper explores the field of space robotics regarding the kinematics, dynamics and control of manipulators mounted onto spacecraft. The core of the issue concerns the spacecraft mounting which reacts in response to the motion of the manipulator. We favour the implementation of spacecraft attitude stabilisation to ease some of the computational issues that will become critical as increasing level of autonomy are implemented. We review issues concerned with physical manipulation and the problem of multiple arm operations. We conclude that space robotics is well-developed and sufficiently mature to tackling tasks such as active debris removal.

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“…Robert et al [22] was the first to apply reinforcement learning to the video transmission area, solving the coding rate control problems of the WLAN transmission process for video and images in the medical field. Pradhan and Subudhi [23] proposed a real-time adaptive control for a flexible manipulator using reinforcement learning approach. Mastronarde and van der Schaar [24] proposed a fast reinforcement learning algorithm for energy-efficient wireless communication network.…”

Section: Reinforcement Learning (Rl) Approachmentioning

confidence: 99%

Adaptive Video Transmission Control System Based on Reinforcement Learning Approach Over Heterogeneous Networks

Cheng

Yang

Wang

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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Video may pass through various types of heterogeneous networks during the process of transmission, which has adverse impacts on the real-time video quality. Traditional methods focus on how to compress videos based on the video flow without considering the real-time network information. This paper presents an adaptive method that combines video encoding and the video transmission control system over heterogeneous networks. This method includes the following steps: first, to collect and standardize the real-time information describing the network and the video, then to assess the video quality and calculate the video coding rate based on the standardized information, and then to process the encoded compression of the video according to the calculated coding rate and transfer the compressed video. The experiments show that there is a significant improvement for the quality of real-time videos transmission without changing the existing network, particularly the core equipment. Our solution is easy to deploy and implement quickly and may help to extensively ensure video quality for normal users. Notice to Practitioners-The main objective of this work is to provide an adaptive video transmission control system and methodology to improve the real-time video quality, which takes the realtime network information into the video transmission control over heterogeneous networks.Our solution is an application-layer protocol and includes three phases: 1) to collect the network and video flow status simultaneously; 2) to adjust the parameters for video quality dynamically that come from the network and video environment feedback; and 3) to optimize the video coding rate that is in accordance with the current environment conditions. Our solution is easy to deploy and implement quickly, may help to extensively ensure video quality for normal users.Index Terms-Adaptive, heterogeneous networks, neural networks, reinforcement learning, video transmission control.

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Real-Time Adaptive Control of a Flexible Manipulator Using Reinforcement Learning

Cited by 89 publications

References 11 publications

Advanced Intelligent Systems for Surgical Robotics

Advanced Intelligent Systems for Surgical Robotics

Tutorial Review on Space Manipulators for Space Debris Mitigation

Adaptive Video Transmission Control System Based on Reinforcement Learning Approach Over Heterogeneous Networks

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