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Design and Integration of Sensors for Control, Planning and Deployment...
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Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3066

Special Issue Editors

Dr. Xianqiang Bao

E-Mail Website
Guest Editor
Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Heisenbergstr. 3, 70569 Stuttgart, Germany
Interests: medical robotics; magnetically controlled microrobots; soft robotics; force sensor; force feedback; ultrasound imaging; fetal imaging; force control; robotic system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuangyi Wang

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190 Beijing, China
Interests: medical robotics; robot-assisted intervention; computer-assisted surgery; sensing and navigation
Dr. James Housden

E-Mail Website
Guest Editor
Biomedical Engineering Department, King’s College London, Westminster Bridge Road, SE1 7EH London, UK
Interests: medical robotics, surgical robotics, robotic navigation, ultrasound imaging, fetal imaging, cardiac imaging

Special Issue Information

Dear Colleagues,

In an era where robotics is transcending traditional boundaries, the role of sensors in enhancing robotic capabilities has never been more pivotal. The design and integration of advanced sensors are crucial for enabling robots to navigate complex environments, make autonomous decisions, and interact intelligently with their surroundings. This evolution marks a significant leap towards creating more adaptive, responsive, and functional robotic systems capable of tackling real-world challenges across various domains. The importance of sensing technologies cannot be overstated, as they pave the way for next-generation robotics to perform more complex, adaptive, and collaborative tasks. Despite the remarkable progress, the field still faces significant challenges that hinder the full realization of sensor-enabled robotics' potential. 

This Special Issue of Sensors (MDPI), entitled "Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems", focuses on the critical aspects of sensor design, manufacturing, and their integration into robotic systems for enhanced control, planning, and deployment. We invite research and review papers that introduce novel developments in sensing technologies, fabrication processes, and their application in robotics.

Dr. Xianqiang Bao
Prof. Dr. Shuangyi Wang
Dr. James Housden
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor design
  • advanced sensor technologies
  • sensor fabrication methods
  • robotic control systems
  • autonomous decision-making
  • sensor fusion technologies
  • flexible sensors
  • smart sensing materials
  • multimodal sensor integration
  • environmental sensing

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Published Papers (3 papers)

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Research

23 pages, 6025 KiB  
Article
Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization
by Sunzid Hassan, Lingxiao Wang and Khan Raqib Mahmud
Sensors 2024, 24(24), 7875; https://doi.org/10.3390/s24247875 - 10 Dec 2024
Viewed by 532
Abstract
Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot [...] Read more.
Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional ‘olfaction-only’ OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the ‘High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the ‘Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based ‘olfaction-only’ and ‘vision-only’ navigation algorithms, and a supervised learning-based ‘vision and olfaction fusion’ (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments. Full article
(This article belongs to the Special Issue Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems)
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11 pages, 4400 KiB  
Article
Page Turning Using Assistive Robot with Low-Degree-of-Freedom Hand
by Hidetoshi Ikeda, Yuta Mizukami, Masahiro Sakamoto, Takumi Saeki, Hokyoo Lee and Masakazu Hori
Sensors 2024, 24(19), 6162; https://doi.org/10.3390/s24196162 - 24 Sep 2024
Viewed by 784
Abstract
This paper proposes a page-turning strategy using an assistive robot that has a low-degree-of-freedom robotic hand. The robotic hand is based on human object handling characteristics, which significantly reduces the number of fingers and joints required to handle various objects. The robotic hand [...] Read more.
This paper proposes a page-turning strategy using an assistive robot that has a low-degree-of-freedom robotic hand. The robotic hand is based on human object handling characteristics, which significantly reduces the number of fingers and joints required to handle various objects. The robotic hand has right and left planar fingers that can transform their shape to handle various objects. To turn a page, the robot uses the planar fingers to push the surface of the page and then rotates the fingers. The design concept, mechanism, sensor system, strategy for page turning, and control system of the robotic hand are presented. The experimental results show that the robot can turn pages using the proposed method; however, it sometimes failed to turn the page when the robotic hand height was too low and too close to the book because the rotation of the fingers was stopped by the book. When the hand detects excessive force during page turning, the control system changes the shape of the fingers and releases the force from the book. The experimental results show the effectiveness of the control system. Full article
(This article belongs to the Special Issue Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems)
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14 pages, 12144 KiB  
Article
NMC3D: Non-Overlapping Multi-Camera Calibration Based on Sparse 3D Map
by Changshuai Dai, Ting Han, Yang Luo, Mengyi Wang, Guorong Cai, Jinhe Su, Zheng Gong and Niansheng Liu
Sensors 2024, 24(16), 5228; https://doi.org/10.3390/s24165228 - 13 Aug 2024
Viewed by 1169
Abstract
With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems [...] Read more.
With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems without overlapping parts is inaccurate. Furthermore, the potential of feature matching points and their spatial extent in calculating the extrinsic parameters of multi-camera systems has not yet been fully realized. To this end, we propose a multi-camera calibration algorithm to solve the problem of the high-precision calibration of multi-camera systems without overlapping parts. The calibration of multi-camera systems is simplified to the problem of solving the transformation relationship of extrinsic parameters using a map constructed by multiple cameras. Firstly, the calibration environment map is constructed by running the SLAM algorithm separately for each camera in the multi-camera system in closed-loop motion. Secondly, uniformly distributed matching points are selected among the similar feature points between the maps. Then, these matching points are used to solve the transformation relationship between the multi-camera external parameters. Finally, the reprojection error is minimized to optimize the extrinsic parameter transformation relationship. We conduct comprehensive experiments in multiple scenarios and provide results of the extrinsic parameters for multiple cameras. The results demonstrate that the proposed method accurately calibrates the extrinsic parameters for multiple cameras, even under conditions where the main camera and auxiliary cameras rotate 180°. Full article
(This article belongs to the Special Issue Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems)
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