AUTONOMOUS ROBOTS

Scope & Guideline

Exploring the Frontiers of Autonomous Systems.

Introduction

Welcome to the AUTONOMOUS ROBOTS information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of AUTONOMOUS ROBOTS, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0929-5593
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1994 to 2024
AbbreviationAUTON ROBOT / Auton. Robot.
Frequency8 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The journal 'AUTONOMOUS ROBOTS' focuses on advancing the field of robotics through innovative research and developments. It encompasses a wide range of topics related to the design, control, and application of autonomous robots, emphasizing both theoretical and practical aspects.
  1. Autonomous Navigation and Control:
    Research in this area includes algorithms and systems for enabling robots to navigate and operate independently in various environments, incorporating techniques from reinforcement learning, path planning, and sensor fusion.
  2. Human-Robot Interaction (HRI):
    This focus area examines how robots can effectively work alongside humans, including understanding human intentions, learning from human interactions, and adapting behaviors in collaborative tasks.
  3. Multi-Robot Systems and Swarm Robotics:
    Studies involve the coordination and collaboration of multiple robots to achieve complex tasks, exploring decentralized control, communication strategies, and emergent behaviors.
  4. Robotic Perception and Sensing:
    This includes advancements in the perception capabilities of robots, such as visual, auditory, and haptic sensing, to enhance their understanding of the environment and improve interaction capabilities.
  5. Machine Learning and Artificial Intelligence in Robotics:
    A significant emphasis is placed on applying machine learning techniques for improving robot performance, including learning from demonstrations, reinforcement learning, and data-driven decision-making.
  6. Robustness and Adaptability in Robotics:
    Research focuses on developing methods that enable robots to adapt to changing environments and unforeseen circumstances, ensuring reliable operation in real-world scenarios.
The journal has seen a surge in specific themes that reflect the evolving landscape of robotics research. These emerging scopes highlight innovative approaches and technologies that are shaping the future of autonomous systems.
  1. Integration of Large Language Models (LLMs):
    Recent studies have increasingly focused on the application of LLMs in robotics, particularly for enhancing natural language understanding, task planning, and human-robot communication.
  2. Reinforcement Learning and Imitation Learning:
    There is a growing trend towards utilizing reinforcement learning and imitation learning techniques, particularly in complex tasks such as navigation and manipulation, which allow robots to learn from real-world interactions.
  3. Robotics in Healthcare and Assistance:
    Research addressing the use of autonomous robots in healthcare settings, including robotic assistance systems and rehabilitation robots, has gained traction, driven by the demand for innovative solutions in eldercare and medical support.
  4. Self-Supervised Learning and Adaptation:
    Emerging themes include self-supervised and semi-supervised learning approaches that enable robots to improve their performance through interaction with their environment, reducing the need for extensive labeled datasets.
  5. Robustness in Uncertain Environments:
    There is an increasing emphasis on developing algorithms that allow robots to operate reliably under uncertainty, particularly in applications involving exploration, mapping, and human-robot collaboration.

Declining or Waning

While the journal continues to thrive in various areas, some themes have shown a decline in prominence over recent years. These waning scopes reflect shifting interests within the robotics community as new technologies and methodologies emerge.
  1. Traditional Robotics Control Methods:
    There has been a noticeable decrease in publications focusing solely on classical control methods, such as PID controllers, as the field shifts towards more adaptive and learning-based approaches.
  2. Basic Robotic Manipulation Techniques:
    Research centered around fundamental manipulation techniques without integration of advanced perception or learning has waned, as there is a growing preference for studies that incorporate complex interactions and contextual awareness.
  3. Static Environment Robotics:
    The exploration of robots operating in static, controlled environments has decreased, with more emphasis now placed on dynamic environments that require real-time adaptability.

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