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Special Issue "Intelligent Systems and Sensors for Robotics"
Deadline for manuscript submissions: 31 May 2020.
Interests: machine learning; embedded systems; intelligent systems for robotics
Special Issues and Collections in MDPI journals
The effective performance of advanced robotic systems greatly depends on two components: a sensing system that can provide valuable and accurate information about the environment, and an intelligent processing system that can properly utilize such information to improve the ability of robots to handle ever more complex tasks.
Machine learning (ML) models provide an enabling technology in support of such intelligent processing systems. The capability of ML to learn from data an inference function represents a key strength for developing robots that are expected to become autonomous and make real-time decisions. This capability in turn enhances the role of sensors in empowering robotics, from industrial robotic systems to humanoid robots.
Bringing ML to embedded systems becomes indeed a requirement for building the next generation of robots. On the other hand, given the constraints imposed by robotics in terms of power consumption, latency, size, and cost, the deployment of a ML model on an embedded system poses major challenges. The main goal is to profit from efficient inference functions that can run on resource-constrained edge devices. Under such paradigm, training might in principle be demanded to a different, more powerful platform. Nonetheless, a more demanding goal is to be able to complete also the training on resource-constrained devices.
This Special Issue will focus on machine learning based models and methodologies for real-time decision making on advanced robotic systems. The aim is to collect the most recent advances in machine learning research for low-resource embedded systems. Accordingly, the Special Issue welcomes methods and ideas that emphasize the impact of embedded machine learning on robotic technologies.
The topics of interest for this special issue include, but are not limited to:
- embedded machine learning
- low-power inference engines
- software/hardware techniques for machine learning
- online learning on resource-constrained edge devices
- power-efficient machine learning implementations on FPGAs
- on-chip training of deep neural networks
- high-performance, low-power computing for deep learning and computer vision
- high-performance, low-power computing for deep learning-based audio and speech processing
- intelligent sensors
- machine learning for sensing and perception
- machine learning for intelligent autonomous systems
Prof. Paolo Gastaldo
Dr. Lin Wang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- embedded machine learning
- intelligent systems
- robot sensing and perception
- machine vision
- autonomous robots
- edge computing