828 research outputs found
Clustering-Based Robot Navigation and Control
In robotics, it is essential to model and understand the topologies of configuration spaces in order to design provably correct motion planners. The common practice in motion planning for modelling configuration spaces requires either a global, explicit representation of a configuration space in terms of standard geometric and topological models, or an asymptotically dense collection of sample configurations connected by simple paths. In this short note, we present an overview of our recent results that utilize clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures in generally complex-shaped and high-dimensional configuration spaces of robotic systems. We demonstrate some potential applications of such clustering tools to the problem of feedback motion planning and control. In particular, we briefly present our use of hierarchical clustering for provably correct, computationally efficient coordinated multirobot motion design, and we briefly describe how robot-centric Voronoi diagrams can be used for provably correct safe robot navigation in forest-like cluttered environments, and for provably correct collision-free coverage and congestion control of heterogeneous disk-shaped robots.For more information: Kod*la
Human-Machine Interfaces for Service Robotics
L'abstract è presente nell'allegato / the abstract is in the attachmen
Medical robots with potential applications in participatory and opportunistic remote sensing: A review
Among numerous applications of medical robotics, this paper concentrates
on the design, optimal use and maintenance of the related technologies in
the context of healthcare, rehabilitation and assistive robotics, and provides
a comprehensive review of the latest advancements in the foregoing field of
science and technology, while extensively dealing with the possible applications of participatory and opportunistic mobile sensing in the aforementioned domains. The main motivation for the latter choice is the variety
of such applications in the settings having partial contributions to functionalities such as artery, radiosurgery, neurosurgery and vascular intervention.
From a broad perspective, the aforementioned applications can be realized via
various strategies and devices benefiting from detachable drives, intelligent
robots, human-centric sensing and computing, miniature and micro-robots.
Throughout the paper tens of subjects, including sensor-fusion, kinematic,
dynamic and 3D tissue models are discussed based on the existing literature
on the state-of-the-art technologies. In addition, from a managerial perspective, topics such as safety monitoring, security, privacy and evolutionary
optimization of the operational efficiency are reviewed
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
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