Real-Time Machine Learning Applications In Mobile Robotics

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Real-Time Machine Learning Applications In Mobile Robotics.

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human-robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has improved since the appearance of modern machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications, including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as Graphics Processing Units (GPUs), have made numerous robotics applications feasible that were not possible previously.

Despite recent advances, there are still gaps in applying available machine learning methods to real robots. Directly transferring algorithms that work successfully in the simulation to the real robot and self-learning of robots are among the current challenges. Moreover, there is also a need for new real-time algorithms and more explainable and interpretable models that receive and process data from the sensors such as cameras, Light Detection and Ranging (LIDAR), Inertial Measurement Unit (IMU), and Global Positioning System (GPS), preferably in an unsupervised or semi-supervised fashion.

This Special Section in IEEE Access aims to present the works relating to the design and usage of the real-time machine learning methods on all mobile robots including legged and humanoid platforms, focusing on state-of-the-art methods, such as deep learning, generative adversarial networks, scalable evolutionary algorithms, reinforcement learning, probabilistic graphical models, Bayesian methods, and explainable and interpretable approaches. The Special Section will present original research articles covering the implementations and applications of mobile robots and incorporating up-to-date results, theorems, algorithms, and systems.

The topics of interest include, but are not limited to:

  • Robotic learning by simulations
  • Learning and adaptive algorithms for robotic mobile manipulators and humanoid robots
  • Human-robot interaction, learning from human demonstrations
  • Learning for human-robot collaborative tasks
  • Autonomous grasping and manipulation by using mobile robots
  • Multi-robot systems, networked robots, and robot soccer
  • Control, complex action learning, and predictive learning from sensorimotor information for bio-inspired social robots
  • Autonomous driving, navigation, planning, mapping, localization, collision avoidance, and exploration
  • Robotics and autonomous system design and implementation
  • Nonlinear control and visual servoing in robotic systems
  • Soft robotics
  • Usage of sensors, such as EEG, ECG, and IMU in robotics
  • Usage of explainable machine learning and interpretable artificial intelligence in robotics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

Associate Editor:  Aysegul Ucar, Firat University, Turkey

Guest Editors:

    1. Jessy W. Grizzle, University of Michigan, USA
    2. Maani Ghaffari Jadidi, University of Michigan, USA
    3. Mattias Wahde, Chalmers University of Technology, Sweden
    4. Levent Akin, Bogazici University, Turkey
    5. Jacky Baltes, National Taiwan Normal University, Taiwan
    6. Işıl Bozma, Bogazici University, Turkey
    7. Jaime Valls Miro, University of Technology Sydney, Australia

Relevant IEEE Access Special Sections:

  1. Advances in Machine Learning and Cognitive Computing for Industry Applications
  2. Integrative Computer Vision and Multimedia Analytics
  3. Uncertainty Quantification in Robotic Applications


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: agulucar@firat.edu.tr.

Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms.

Robot swarm is one of the hottest topics in both robotics and artificial intelligence, and exciting progress is being achieved. As the key enablers in practical robot swarms, communication and networking are attracting attention. Most applications consider centralized control and reliable communication infrastructure, in order to avoid the significantly increased complexity of distributed task allocation, formation control and collision avoidance in robot swarms.

There are many challenges and problems that are yet to be solved in developing real-world applications of wireless connected robot swarms. For example, collaborations of heterogeneous robot swarms need to function reliably and robustly in the absence of communication infrastructures in remote areas or post-disaster rescues. The research of communications and networking for wireless-connected robot swarms demands joint efforts in robotic and communications disciplines. The objective is to develop technologies that enable efficient management of wireless spectrum resources and highly-networked intelligent behaviors to achieve the full potential of wireless-connected robot swarms.

This Special Section in IEEE Access aims to present recent developments in communications and networking for wireless connected intelligent robot swarms, and their applications, as well as to provide a reference for future research of wireless communication and networking, and their integration with autonomous robotics. The contributions of this Special Section will cover a wide range of research and development topics relevant to autonomous robotic design, cognitive communications, cognitive networking and artificial intelligence. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on the analysis, modeling, simulations and field experiments, as well as articles that can fill the gap between theoretical contributions on intelligent swarms and practical demonstrations and applications.

The topics of interest include, but are not limited to:

  • Channel modeling and simulation for wireless connected robot swarms
  • Cognitive PHY and MAC protocol design for wireless connected robot swarms
  • Ad hoc networking for wireless connected robot swarms
  • Decentralized control and distributed protocol design for wireless connected robot swarms
  • Energy scavenging and power transfer techniques for wireless connected robot swarms
  • Data-driven optimization of wireless networks for robot swarms
  • Joint design of wireless communications and autonomous robot behaviours, e.g. networked control, network-based fault detection and tolerance, path planning, formation control, data sharing without explicit wireless communications etc.
  • Testbeds and experimental evaluations for communications and networking in wireless-connected robot swarms
  • Field demonstrations and applications of aerial, ground and underwater robotic swarms
  • Resource allocation in wireless-connected robot swarms
  • Applications of deep learning techniques in wireless connected robot swarms
  • Transfer learning and reinforcement learning for networking and communications of robot swarms in complex unknown and unexplored environments
  • Maintaining wireless communication-connectivity in wireless-connected robot swarms
  • Underwater robotic swarm communications and networking design
  • Control algorithm and behavior issues in wireless-connected robot swarms
  • Distributed sensing and precise mapping in wireless-connected robot swarms
  • Effect of smart sensing technologies on communications in wireless-connected robot swarms
  • Control, formation and navigation in wireless-connected robot swarms
  • Swarm intelligence in wireless-connected robot swarms
  • Cooperative robotic swarms for Internet-of-Things ecosystems

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Jiankang Zhang, University of Southampton, UK

Guest Editors:

  1. Bo Zhang, National Innovation Institute of Defense Technology, China
  2. DaeEun Kim, Yonsei University, Korea
  3. Hui Cheng, Sun Yat-sen University, China
  4. Jinming Wen, University of Toronto, Canada
  5. Luciano Bononi, University of Bologna, Italy
  6. Venanzio Cichella, University of Iowa, USA

 

Relevant IEEE Access Special Sections:

  1. Networks of Unmanned Aerial Vehicles: Wireless Communications, Applications, Control and Modelling
  2. Network Resource Management in Flying Ad Hoc Networks: Challenges, Potentials, Future Applications, and Wayforward
  3. Artificial Intelligence and Cognitive Computing for Communications and Networks


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: jz09v@ecs.soton.ac.uk.

Performance Evaluation of Multi-UAV System in Post-Disaster Application….

Can eyes in the air counter chaos on the ground? Researchers in Japan analyzed performance of unmanned aerial vehicles (UAVs) used in the response to the 2011 Tohoku earthquake-tsunami disaster, and report on their findings in this IEEE Access article of the week.

The paper proposes an evaluation of unmanned aerial vehicles (UAVs) performance in the mapping of disaster-struck areas. Sendai city in Japan, which was struck by the Tohoku earthquake/tsunami disaster in 2011, was mapped using multi-heterogeneous UAV.

Normal mapping and searching missions are challenging as human resources are limited, and rescue teams are always needed to participate in disaster response mission. Mapping data and UAV performance evaluation will help rescuers to access and commence rescue operations in disaster-affected areas more effectively.

Herein, flight-plan designs are based on the information recorded after the disaster and on the mapping capabilities of the UAVs. The numerical and statistical results of the mapping missions were validated by executing the missions on real-time flight experiments in a simulator and analyzing the flight logs of the UAVs.

After considering many factors and elements that affect the outcomes of the mapping mission, the authors provide a significant amount of useful data relevant to real UAV modules in the market. All flight plans were verified both manually and in a hardware-in-the-loop simulator developed by the authors. Most of the existing simulators support only a single UAV feature and have limited functionalities such as the ability to run different models on multiple UAVs.

The simulator demonstrated the mapping and fine-tuned flight plans on an imported map of the disaster. As revealed in the experiments, the presented results and performance evaluations can effectively distribute different UAV models in post-disaster mapping missions.

View this article n IEEE Xplore

Additive Manufacturing Security

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Additive Manufacturing Security.

Additive Manufacturing (AM), a.k.a. 3D Printing, is a rapidly growing multi-billion dollar industry. This technology is being used to manufacture 3D objects for a broad range of application scenarios such as prototypes in R&D and functional parts in safety critical systems. The benefits of this technology include shorter design-to-product time, just-in-time and on-demand production, and close proximity to assembly lines. Furthermore, AM can produce functional parts with complex internal structures and optimized physical properties with less material waste than subtractive manufacturing.

Due to the numerous technical and economic advantages that this technology promises, AM is expected to become a dominant manufacturing technology in both industrial and home settings. The need to secure physical and cyber-physical systems gives rise to a corresponding need to understand potential attacks via AM systems, and to develop countermeasures that will enable attack prevention, detection, and digital investigation. So far, three major security threat categories have been identified for AM: theft of technical data (or violation of intellectual property, IP), sabotage of AM, and manufacturing of illegal objects. AM Security is a fairly new and highly multi-disciplinary field of research that addresses these threats.

The aim of this Special Section in IEEE Access is to discuss recent advances in AM Security, addressing both offensive and defensive approaches.

The topics of interest include, but are not limited to:

  • Compromise of AM systems and environment
  • Security threats and attacks in AM context: Theft of technical data (or violation of intellectual property), sabotage of AM, manufacturing of illegal objects
  • Technical approaches to detect attacks on/with AM
  • Technical approaches to prevent attacks on/with AM
  • Digital Investigation and [digital] forensics in the AM context
  • Legal aspects of attacks on/with AM
  • Economic incentives for AM Security
  • Socioeconomic implications of attacks on/with AM
  • Comparative analysis of AM vs. CPS/IoT/Industry 4.0/… Securities

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Mark Yampolskiy, Auburn University, USA

Guest Editors:

  1. Mohammad Al Faruque, University of California Irvine, USA
  2. Raheem Beyah, Georgia Tech, USA
  3. William Frazier, Naval Air Systems, USA
  4. Wayne King, The Barnes Group Advisors, USA
  5. Yuval Elovici, Ben-Gurion University of the Negev, Israel
  6. Anthony Skjellum, University of Tennessee at Chattanooga, USA
  7. Joshua Lubell, National Institute of Standards and Technology (NIST), USA
  8. Celia Paulsen, National Institute of Standards and Technology (NIST), USA

 

Relevant IEEE Access Special Sections:

  1. Cyber-Physical Systems
  2. Collaboration for Internet of Things
  3. Towards Service-Centric Internet of Things (IoT): From Modeling to Practice


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: yampolskiy@southalabama.edu.

Cloud-based Robotic Systems for Intelligent Services

Submission Deadline: 1 July 2018

IEEE Access invites manuscript submissions in the area of Cloud-based Robotic Systems for Intelligent Services.

Recent advances in sensor/actuator as well as artificial intelligence (AI) technologies have made it possible for mobile robots such as autonomous automobiles and autonomous unmanned aerial vehicles to go about performing their tasks in varied environments. With wireless communications, these mobile robots can be connected to each other to exchange information, coordinate their movements, and cooperate to perform more extensive tasks, forming robotic systems. Using wireless communications, such robotic systems can further be connected to cloud computing services via the mobile Internet, which offers the potential to significantly enhance the capabilities of such robotic systems. Thus cloud-based robotic systems offer great promises for intelligent services beyond the capabilities of current robots or robotic systems.

First, robot systems employing advanced AI techniques that leverage multiple layer artificial neural networks for deep learning can enable intelligent services that learn from past experience to plan a course of actions that optimizes some task objectives, e.g., minimizing energy consumption, for the current environmental conditions. However, these machine learning techniques are computation intensive and may not be well supported by individual robotic systems. In contrast, cloud computing services offer virtually unlimited computation resources on-demand in a scalable manner, greatly facilitating the use of advanced AI techniques in robotic systems. Second, widespread deployment of robotic systems employing a large number of sensors results in a massive amount of data being generated over short periods of time. Cloud-based big data analytics can be employed to derive useful information to enhance the utility of cloud-based robotic systems. For example, applying big data analytics to data collected from a large number of cloud-based robotic systems, a manufacturer may be able to determine that a batch of sensors manufactured by this company is defective. Third, it is conceivable that in the future distributed general purpose robotic units connected to the cloud can be dynamically configured and programmed to form logical robotic systems under software control to perform specific services in a virtualized manner, i.e., cloud-based robotic systems can provide software-defined robotic system as a service.

Cloud computing platforms would be crucial to enable a programming environment capable of fast service creation, as well as an operational and management environment to ensure that these intelligent robotic services can operate reliably and be properly managed.

Based on the above observations, we can see that cloud-based robotic systems offer great potential for intelligent services in both the short and longer term, but there are many technical challenges that need to be addressed.

Some of the technical challenges and potential applications of cloud-based robotic systems include but are not limited to:

  1. Cloud-based big data analytics mechanisms;
  2. Cooperative mechanisms to coordinate the information of robotic systems and share updates on detected changes in the environment;
  3. Architectures, programming framework, management and control mechanisms to enable robotic function virtualization;
  4. Robotic edge computing to complement the cloud in satisfying hard real time interaction needs;
  5. Robot-assisted healthcare, especially for shut-in and elderly patients, with monitoring, diagnostic and simple treatment capabilities; by sampling data from sensors for body to the cloud system, using data mining and machine learning techniques;
  6. Smart homes, offices and factories equipped with cloud-based robotic systems for enhanced security, energy efficiency, work throughput, occupant comfort, etc.

The main objective of this Special Section in IEEE Access is to collect multidisciplinary research contributions on technological breakthrough and advancement towards cloud-based robotic systems for intelligent services. Topics explored in this Special Section include, but are not limited, to the following aspects of intelligent services involving cloud-based robotic systems:

  • Cloud computing technologies
  • Cooperative robotic systems
  • Multi-modal robotic cognition
  • Cooperative communications among robots
  • Real-time big data analytics of customers
  • Data mining techniques
  • Cloud architecture and cloud storage
  • Mobile social networks
  • Instance detection and recognition in robotic system
  • Image and scene classification in robotic system
  • Semantic interpretation in robotic system
  • Robot function virtualization
  • Robotic edge computing

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Prof. Xiping Hu, Chinese Academy of Sciences, China

Guest Editors:

  1. Victor C.M. Leung, University of British Columbia, Canada
  2. Adnan Al-Anbuky, Auckland University of Technology, New Zealand
  3. Ken Goldberg, University of California, Berkeley, USA
  4. Hesheng Wang, Shanghai Jiao Tong University, China
  5. Fei Wang, Cornell University, USA
  6. Jianwei Zhang, University of Hamburg, German

 

Relevant IEEE Access Special Sections:

  1. Trends and Advances for Ambient Intelligence with Internet of Things Systems
  2. Big Data Analytics in Internet-of-Things and Cyber-Physical System
  3. Industry 4.0


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: xp.hu@siat.ac.cn

Industry 4.0

Submission Deadline: 20 September 2016

IEEE Access invites manuscript submissions in the area of Industry 4.0.

Industry 4.0 is a recently emerging buzzword that gains significant interest among all stakeholders of the global industry-related R&D market from the academia to worldwide companies. It is a typical business that attracts everyone yet the definitions are not very matured. It is an amazing melting pot of disruptive technologies with easy grip to put it on the flag.

No doubt, to maximize the impact of Industry 4.0, researchers from different fields and industrial people have to work shoulder to shoulder applying the awesome inventions in practice. On the top of the wave, it is timely to analyze who will benefit from the novel achievements and how.

With defining the scope of the Special Section in IEEE Access, we also make an attempt to grasp the main directions within Industry 4.0. Arbitrary mixtures of the following topics are welcome in form of original research, survey or epistemological works.

1. Utilization of the latest mechatronics in manufacturing processes

  • Flexible automation, Robotics, Human Machine co-working, autonomous transportation, etc.

2. Extensive data collection and storage

  • Continuous measurement and tracking
  • Logging and analysis of human activity in production
  • Factory-wide monitoring of internal state of industrial controllers
  • Storage and organization of the collected data
  • Data-driven modelling for design, analysis and prediction

3. Big Data analytics

  • Searching for higher order relationship in collected data with various aims.
  • Fault forecast
  • Identification of bottlenecks
  • Identification of surplus capacities
  • Detection of Human failures and bad practices

4. Feedback to industrial processes

  • Real-time process optimization: scheduling, logistics, etc.
  • Optimal maintenance scheduling
  • Fast intervention in fault situations

5. Support of human

  • New generation of interactive displays: Virtual and Augmented Reality
  • Reduction of cognitive load
  • Advances toward the augmented human

6. Technology-based support of high added value processes in management and design

  • Knowledge representation and user interfaces
  • Tools for distributed teamwork
  • Virtual teams, virtual enterprise
  • Process parameters optimization
  • Technologies for building twin model of analysis and design

7. New challenges of security

  • Information Privacy, Security and Data Integrity
  • Means of vulnerability
  • Physical security (Human machine coexistence)

Researchers, engineers and all representatives of academia and industry are encouraged to submit their articles and contribute to the progress of Industry 4.0. This is a very trending topic, since billions of public and private funding are available for industrial innovation naturally related to Industry 4.0.

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Shun-Feng Su, National Taiwan University of Science and Technology, Taiwan

Guest Editors:
1. Imre J. Rudas, Óbuda University, Hungary
2. Jacek M. Zurada, University of Louisville, USA
3. Meng Joo Er, Nanyang Technology University, Singapore
4. Jyh-Horng Chou, National Kaohsiung University of Applied Sciences, Taiwan
5. Daeil Kwon, Ulsan National Institute of Science and Technology, Korea

 

IEEE Access Editor in Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access (Phone: (732) 562-6036, specialsections@ieee.org)