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Innovative Synergies: Robotics, AI, and Sensor Technologies in Field Autonomous Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2334

Special Issue Editors


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Guest Editor
School of Engineering, RMIT University, Melbourne, Australia
Interests: bioinspired robot; field robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence-powered robotics is destined to unlock unprecedented capabilities and functionalities in autonomous systems. As this integration of the two fields further develops, the emphasis will fall on creating intelligent machines that perform complex tasks but are also capable of learning autonomously in adapting to unstructured environments. Field autonomous systems integrate robotics, AI, and sensor technologies. Thus, there are various difficulties and limits to deploying them effectively, such as designing bioinspired or swarm robots so that they can adapt to varying terrains, weather conditions, and obstacles. In dynamic and unstructured field environments, achieving high levels of autonomy and intelligent decision-making is another difficult task, as is human–robot interaction, which is intrinsically different from interaction with traditional computers or tools. Validation and verification processes constitute an integral element of operationally acceptable and advanced AI-powered robotic systems. An additional obstacle exists in the form of knowledge transfer and learning from humans. The Special Issue encompasses the understanding and development of robotics and artificial intelligence applications in manufacturing, agriculture, construction, healthcare, hospitality, and other industries.

The scope of this Special Issue includes (but is not limited to) the following:

  • Field robotics systems: trials and real-world applications;
  • Robotic sensor technologies and sensor fusion for field deployment;
  • AI applications in robotics, vision, and sensor technologies;
  • Algorithms and field implementations for visual and non-visual sensors;
  • Mobile robotics and navigation in field environments;
  • Human–robot interactions in field settings;
  • Bio-inspired robotics for field applications;
  • Computational intelligence in field robotics;
  • Mechatronic systems: field applications and innovations;
  • Swarm robotics in field operations;
  • Drones in field operations;
  • Social/humanoid robotics in field contexts;
  • Immersive interfaces for field robotics;
  • IoT and robotics: crossover field applications.

Dr. Ehsan Asadi
Dr. Hamid Khayyam
Guest Editors

Manuscript Submission Information

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Keywords

  • field robotics systems: trials and real-world applications
  • robotic sensor technologies and sensor fusion for field deployment
  • AI applications in robotics, vision, and sensor technologies
  • algorithms and field implementations for visual and non-visual sensors
  • mobile robotics and navigation in field environments
  • human–robot interactions in field settings
  • bio-inspired robotics for field applications
  • computational intelligence in field robotics
  • mechatronic systems: field applications and innovations
  • swarm robotics in field operations
  • drones in field operations
  • social/humanoid robotics in field contexts
  • immersive interfaces for field robotics
  • IoT and robotics: crossover field applications

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Published Papers (3 papers)

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Research

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22 pages, 7705 KiB  
Article
Implementation of SLAM-Based Online Mapping and Autonomous Trajectory Execution in Software and Hardware on the Research Platform Nimbulus-e
by Thomas Schmitz, Marcel Mayer, Theo Nonnenmacher and Matthias Schmitz
Sensors 2025, 25(15), 4830; https://doi.org/10.3390/s25154830 - 6 Aug 2025
Viewed by 383
Abstract
This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four [...] Read more.
This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLAB co-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article
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Review

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26 pages, 2457 KiB  
Review
Crack Detection in Civil Infrastructure Using Autonomous Robotic Systems: A Synergistic Review of Platforms, Cognition, and Autonomous Action
by Rong Dai, Rui Wang, Chang Shu, Jianming Li and Zhe Wei
Sensors 2025, 25(15), 4631; https://doi.org/10.3390/s25154631 - 26 Jul 2025
Viewed by 638
Abstract
Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus [...] Read more.
Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus on individual components in isolation and fail to present a complete picture of how these systems work together. This study focuses on robotic crack detection and proposes a structured framework that connects three core modules: the physical platform (robots and sensors), the cognitive core (crack detection algorithms), and autonomous action (navigation and planning). We analyze key technologies, their interactions, and the challenges involved in real-world implementation. The aim is to provide a clear roadmap of current progress and future directions, helping researchers and engineers better understand the field and develop smart, deployable systems for infrastructure crack inspection. Full article
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22 pages, 590 KiB  
Review
ROS-Based Navigation and Obstacle Avoidance: A Study of Architectures, Methods, and Trends
by Zhe Wei, Sen Wang, Kangyelin Chen and Fang Wang
Sensors 2025, 25(14), 4306; https://doi.org/10.3390/s25144306 - 10 Jul 2025
Viewed by 875
Abstract
With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation [...] Read more.
With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems. Full article
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