Frontiers in Robotics and AI

Scope & Guideline

Transforming Ideas into Innovations in Robotics and AI

Introduction

Welcome to your portal for understanding Frontiers in Robotics and AI, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN2296-9144
PublisherFRONTIERS MEDIA SA
Support Open AccessYes
CountrySwitzerland
TypeJournal
Convergefrom 2014 to 2024
AbbreviationFRONT ROBOT AI / Front. Robot. AI
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE CH-1015, SWITZERLAND

Aims and Scopes

Frontiers in Robotics and AI focuses on advancing the field of robotics and artificial intelligence through interdisciplinary research. The journal aims to bridge the gap between theoretical foundations and practical applications, fostering innovation in robotic systems and AI technologies.
  1. Human-Robot Interaction (HRI):
    Research exploring the dynamics of interactions between humans and robots, including social, emotional, and communicative aspects. This includes studies on trust, acceptance, and the role of robots in various social contexts.
  2. Robotic Systems and Control:
    Development and optimization of control algorithms for various robotic systems, including mobile robots, manipulators, and autonomous vehicles. This encompasses methodologies for both model-based and model-free approaches.
  3. Soft Robotics and Bioinspired Design:
    Investigation of soft robotic systems that mimic biological organisms, focusing on their design, actuation, and control. This area emphasizes the application of soft materials and bioinspired mechanisms for versatile robotic functionality.
  4. Assistive and Rehabilitation Robotics:
    Research dedicated to the development of robotic systems aimed at aiding individuals with disabilities or rehabilitation needs. This includes exoskeletons, socially assistive robots, and technologies for enhancing mobility and daily living.
  5. AI and Machine Learning in Robotics:
    Integration of artificial intelligence and machine learning techniques into robotic systems to enhance perception, decision-making, and autonomy. This encompasses deep learning, reinforcement learning, and cognitive robotics.
  6. Robotics in Healthcare:
    Application of robotic technologies in medical settings, including surgical robots, rehabilitation systems, and telemedicine. This area focuses on improving patient outcomes and enhancing healthcare delivery through robotic assistance.
  7. Environmental and Agricultural Robotics:
    Exploration of robotic systems designed for environmental monitoring, agriculture, and natural resource management. This includes autonomous systems for tasks such as crop monitoring, pest control, and sustainable farming practices.
Frontiers in Robotics and AI has identified several trending and emerging themes that reflect the current interests and challenges in the field. These themes highlight the integration of advanced technologies and interdisciplinary approaches.
  1. Cognitive and Social Robotics:
    An increasing focus on the cognitive capabilities of robots, including emotional intelligence and social interaction, reflects a trend towards developing robots that can understand and respond to human emotions and social cues.
  2. AI-Driven Robotic Systems:
    The integration of AI and machine learning techniques into robotics is a significant emerging trend, enhancing robots' ability to learn and adapt to their environments autonomously.
  3. Teleoperation and Remote Robotics:
    The demand for teleoperated robotic systems, especially in healthcare and hazardous environments, has surged, particularly in light of recent global health challenges, emphasizing the need for remote operation capabilities.
  4. Soft Robotics and Flexible Systems:
    The field of soft robotics is gaining momentum, focusing on robots made from compliant materials that can safely interact with humans and navigate complex environments.
  5. Sustainability in Robotics:
    Research addressing the environmental impact of robotics, including energy-efficient designs and sustainable materials, is emerging as a vital area of interest in the quest for eco-friendly robotic solutions.
  6. Robotics for Education and Social Good:
    There is a growing emphasis on using robotics for educational purposes and social impact, particularly in engaging children with learning disabilities or enhancing learning experiences through interactive robots.

Declining or Waning

While Frontiers in Robotics and AI continues to expand its research horizons, certain themes have seen a decline in focus over recent years. This may indicate a shifting landscape in the robotics field as researchers prioritize emerging challenges and technologies.
  1. Traditional Industrial Robotics:
    Research focusing on conventional industrial robots and automation has seen a decline, as the field shifts towards more flexible, collaborative, and human-centered robotic systems.
  2. Single-Task Robotics:
    There has been a waning interest in robots designed for single, specific tasks. The trend is moving towards robots that can perform multiple functions and adapt to various environments and tasks.
  3. Robotics in Extreme Environments:
    While still relevant, the frequency of publications specifically addressing robotics in extreme environments (e.g., space, underwater) has decreased as the focus broadens to encompass more general applications of robotics.
  4. Basic Robot Kinematics and Dynamics:
    Studies centered around fundamental kinematics and dynamics of robots are becoming less prominent, with more emphasis being placed on advanced control methods and AI-driven approaches.
  5. Conventional Robot Perception Methods:
    Traditional perception methods based on rigid sensors and basic algorithms are losing ground to more sophisticated approaches that leverage deep learning and advanced sensor technologies.

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