Interactive Manipulation of Mobile Manipulators

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5231

Special Issue Editors


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Guest Editor
College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Interests: autonomous grasping and dexterous manipulation; sensing; data fusion and estimation for autonomous systems; visuo-tactile servoing control; vision, tactile based object recognition
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Guest Editor
Institute of Intelligent Machines Hefei Institutes of Physical Science, Chinese Academy of Science No. 350 Shushanhu Road, Hefei, China
Interests: intelligent robots; multi-dimensional force sensor; circuit design; signal processing, etc.

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Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Interests: underwater image processing; intelligent robots; underwater robots and robot control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your work to this Special Issue (SI) on “Interactive Manipulation of Mobile Manipulator”. In this SI, we would like to bring together AI and robotics researchers and discuss the scientific foundations, applications, and analysis of mobile manipulator systems.

Originally, mobility and manipulation were two different research branches in the intelligent robots field. For mobility, researchers are interested in how to intelligently and safely move a mobile robot to the desired place even in unseen and unstructured environments. For manipulation, different types of fixed installation manipulators are studied to control external objects, e.g., pick-and-place, grasping, or in-hand/bi-manual manipulation. However, recent robotic applications require more advanced mobile manipulation skills. In some scenarios, robot work with humans in order to perform a task more efficiently.

Promising results have already been achieved in mobile manipulation. In terms of hardware, many new types of mobile robots are available. These include traditional wheel and biped or quadruped robots. Additionally, hybrid wheel and feet robots have been developed in recent years. This can help robot move over dynamic and variant terrains. Many types of collaborative robots have been integrated with mobile robots. In terms of software, innovative coordination technologies between the mobile base and manipulator are studied either via model-based or data-driven approaches. With rich sensor resources, robots can compute the environment model in an interactive way and safely work with human together. From the application perspective, many mobile manipulators are employed in space, ocean, and industrial environments.

We invite experts from different domains to share and report findings on mobile manipulation and discuss its real-life application.

Prof. Dr. Qiang Li
Dr. Huibin Cao
Dr. Xiang Dong
Guest Editors

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Keywords

  • new sensors and mobile robot design and evaluation
  • model, planning, and control of mobile manipulator
  • loco-manipulation
  • active or interactive perception in mobile manipulation
  • shared autonomy for mobile manipulation
  • human–robot collaboration
  • applications in service, industrial, agriculture, space, medical and underwater domains

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Published Papers (2 papers)

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Research

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25 pages, 43246 KiB  
Article
Benchmark Feature Detection Method for Mobile Robot Automatic Drilling System Integrated with Deep Learning
by Jialong Dai, Jianxin Shen, Wei Tian, Pengcheng Li, He Liu and Xiangshun Cui
Machines 2025, 13(2), 154; https://doi.org/10.3390/machines13020154 - 17 Feb 2025
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Abstract
Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, [...] Read more.
Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, this paper proposes a method that uses a 2D industrial camera for image capture, applies deep learning for initial target recognition and positioning, and then determines the feature extraction location based on the initial recognition. The extracted benchmark positions are accurately fitted using an improved Huber algorithm. Experimental results demonstrate that this approach improves the benchmark feature detection recognition rate by 43.8%, center recognition accuracy by 78.26%, and overall hole processing accuracy by 54.69%. Full article
(This article belongs to the Special Issue Interactive Manipulation of Mobile Manipulators)
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Review

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22 pages, 1897 KiB  
Review
A Review of Touching-Based Underwater Robotic Perception and Manipulation
by Jia Sun, Qifeng Zhang, Yu Lu, Bingding Huang and Qiang Li
Machines 2025, 13(1), 41; https://doi.org/10.3390/machines13010041 - 10 Jan 2025
Cited by 1 | Viewed by 3499
Abstract
This review focuses on touching-based underwater robotic perception and manipulation, and provides a comprehensive overview of the current research landscape. We begin by examining underwater tactile sensors, discussing their basic types and recent advancements that have facilitated their integration into underwater robotic manipulation. [...] Read more.
This review focuses on touching-based underwater robotic perception and manipulation, and provides a comprehensive overview of the current research landscape. We begin by examining underwater tactile sensors, discussing their basic types and recent advancements that have facilitated their integration into underwater robotic manipulation. Additionally, we explore the development of force control algorithms for underwater manipulators and grippers, emphasizing their critical role in underwater environments. Furthermore, we analyze the application of force control algorithms in underwater robotic manipulation, considering different autonomy levels, basic manipulation tasks, and specific operational scenarios. Through this investigation, we identify existing limitations and propose future research directions aimed at enhancing the operational capabilities of underwater vehicle manipulator systems (UVMS) and expanding their application range. Finally, this review highlights key challenges and outlines pathways for advancing the field. Full article
(This article belongs to the Special Issue Interactive Manipulation of Mobile Manipulators)
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