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Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition)

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 434

Special Issue Editors


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Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: autonomous robots; robot control; intelligent motion control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: method and technology of the digital design associated with CAD / CAE /Optimisation; modern equipment; structural optimisation; intelligent test
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, University of Leeds, Harrogate, UK
Interests: robot control; motion control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advantages of noteworthy dexterity, maneuverability, and high efficiency in performing a growing variety of tasks, autonomous robots are becoming increasingly intelligent and complex with the ability to complete difficult operations with the comprehensive utilization of multimodal sensors, embodied intelligence frameworks, and large model-driven controllers. Autonomous robots can act in swarms with cognitive reasoning and offering total flexibility for industrial applications, which have expanded significantly from manufacturing and automation to healthcare, agriculture, and human–robot collaboration. This Special Issue aims to provide up-to-date research concepts, theoretical findings, and practical solutions for autonomous robots in relation to AI-augmented perception, embodied decision-making, and adaptive motion control, with a focus on sensorimotor coordination, human–robot value alignment, and safety-critical autonomy. We invite contributions that bridge theoretical advances and practical deployments, aiming to redefine the boundaries of autonomous robotics in the era of embodied intelligence.

Dr. Yuanlong Xie
Prof. Dr. Shuting Wang
Prof. Dr. Shane Xie
Guest Editors

Manuscript Submission Information

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Keywords

  • kinematic and dynamic modelling and parameter identification
  • learning-based perception, recognition, navigation, mapping, and localization
  • generative AI-driven perception and multimodal sensor fusion
  • understanding of intelligent decisions, cooperation, environments, and situations
  • AI-driven multi-agent decision-making (e.g., reinforcement learning, meta-learning)
  • large language model (LLM)-guided task planning and cognitive reasoning
  • foundation models for embodied intelligence and robot-environment interaction
  • mobile robot manipulation and in-wheel-driven techniques
  • cooperative control of multiple autonomous systems
  • coexisting–cooperative–cognitive technologies
  • autonomous levels of unmanned systems
  • robot optimal control, adaptive control, and system optimization
  • model-based and model-free reinforcement learning for adaptive control

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Published Papers (1 paper)

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Research

25 pages, 40263 KiB  
Article
Autonomous Navigation of Mobile Robots: A Hierarchical Planning–Control Framework with Integrated DWA and MPC
by Zhongrui Wang, Shuting Wang, Yuanlong Xie, Tifan Xiong and Chao Wang
Sensors 2025, 25(7), 2014; https://doi.org/10.3390/s25072014 - 23 Mar 2025
Viewed by 348
Abstract
In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework [...] Read more.
In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods. Full article
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