2,615 research outputs found
Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level
Surgical robotics is a rapidly evolving field that is transforming the
landscape of surgeries. Surgical robots have been shown to enhance precision,
minimize invasiveness, and alleviate surgeon fatigue. One promising area of
research in surgical robotics is the use of reinforcement learning to enhance
the automation level. Reinforcement learning is a type of machine learning that
involves training an agent to make decisions based on rewards and punishments.
This literature review aims to comprehensively analyze existing research on
reinforcement learning in surgical robotics. The review identified various
applications of reinforcement learning in surgical robotics, including
pre-operative, intra-body, and percutaneous procedures, listed the typical
studies, and compared their methodologies and results. The findings show that
reinforcement learning has great potential to improve the autonomy of surgical
robots. Reinforcement learning can teach robots to perform complex surgical
tasks, such as suturing and tissue manipulation. It can also improve the
accuracy and precision of surgical robots, making them more effective at
performing surgeries
Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection
Heading towards navigational autonomy in unmanned surface vehicles (USVs) in
the maritime sector can fundamentally lead towards safer waters as well as
reduced operating costs, while also providing a range of exciting new
capabilities for oceanic research, exploration and monitoring. However,
achieving such a goal is challenging. USV control systems must, safely and
reliably, be able to adhere to the international regulations for preventing
collisions at sea (COLREGs) in encounters with other vessels as they navigate
to a given waypoint while being affected by realistic weather conditions,
either during the day or at night. To deal with the multitude of possible
scenarios, it is critical to have a virtual environment that is able to
replicate the realistic operating conditions USVs will encounter, before they
can be implemented in the real world. Such "digital twins" form the foundations
upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV)
algorithms can be used to develop and guide USV control systems. In this paper
we describe the novel development of a COLREG-compliant DRL-based collision
avoidant navigational system with CV-based awareness in a realistic ocean
simulation environment. The performance of the trained autonomous Agents
resulting from this approach is evaluated in several successful navigations to
set waypoints in both open sea and coastal encounters with other vessels. A
binary executable version of the simulator with trained agents is available at
https://github.com/aavek/Aeolus-OceanComment: 22 pages, last blank page, 17 figures, 1 table, color, high
resolution figure
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Control theory provides engineers with a multitude of tools to design
controllers that manipulate the closed-loop behavior and stability of dynamical
systems. These methods rely heavily on insights about the mathematical model
governing the physical system. However, in complex systems, such as autonomous
underwater vehicles performing the dual objective of path-following and
collision avoidance, decision making becomes non-trivial. We propose a solution
using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop
autonomous agents capable of achieving this hybrid objective without having \`a
priori knowledge about the goal or the environment. Our results demonstrate the
viability of DRL in path-following and avoiding collisions toward achieving
human-level decision making in autonomous vehicle systems within extreme
obstacle configurations
Context-aware learning for robot-assisted endovascular catheterization
Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces
Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning
Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK
Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning
This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few.
The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means
of improving the autonomy of marine vehicles over the last few years. This
article surveys the recent ML approaches utilised for ship collision avoidance
(COLAV) and mission planning. Following an overview of the ever-expanding ML
exploitation for maritime vehicles, key topics in the mission planning of ships
are outlined. Notable papers with direct and indirect applications to the COLAV
subject are technically reviewed and compared. Critiques, challenges, and
future directions are also identified. The outcome clearly demonstrates the
thriving research in this field, even though commercial marine ships
incorporating machine intelligence able to perform autonomously under all
operating conditions are still a long way off
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe
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