Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Special Issues
Climbing Robots: Scaling Walls with Precision and Efficiency
share announcement

Climbing Robots: Scaling Walls with Precision and Efficiency

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3083

Special Issue Editors

Dr. Weinan Chen

E-Mail Website
Guest Editor
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510520, China
Interests: robot vision; SLAM; mobile robot
Dr. Haifei Zhu

E-Mail Website
Guest Editor
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510520, China
Interests: bioinspired robot; climbing robot
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuefeng Zhou

E-Mail Website
Guest Editor
Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: robotics; industrial robotics and automation; motion planning; motion control; force control technology
Prof. Dr. Chaoqun Wang

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250101, China
Interests: intelligent perception and navigation; robotics and embodied intelligence; control theory and applications
Prof. Dr. Qiang Li

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of climbing robots has become increasingly relevant as we seek innovative solutions for applications in inspection, maintenance, rescue operations, and the exploration of complex or hazardous environments. The challenges involved in designing robots that can scale vertical surfaces with precision, efficiency, and robustness are immense, encompassing disciplines from robotics and material science to intelligent perception and control systems. This Special Issue, “Climbing Robots: Scaling Walls with Precision and Efficiency”, invites original research and review articles that explore new concepts, methodologies, and technologies in the field of climbing robots.

Topics of Interest
We welcome contributions that address, but are not limited to, the following areas:

  • Mechanisms for Climbing Robots: Novel designs and actuation mechanisms for reliable surface adhesion and locomotion.
  • Material Innovations: Smart materials and surface coatings to enhance adhesion, reduce slippage, and improve durability.
  • Control Systems: Advanced control strategies, including adaptive and autonomous controls, for real-time adjustments to complex surfaces.
  • Sensing and Perception: Sensor integration for environmental awareness, navigation, and obstacle avoidance.
  • Energy Efficiency: Power management strategies and energy-efficient designs to enhance operational longevity.
  • AI and Machine Learning in Climbing: AI-driven decision-making processes for complex navigation and task execution.
  • Applications and Case Studies: Real-world applications of climbing robots, including in construction, inspection, maintenance, disaster recovery, and extraterrestrial exploration.

Dr. Weinan Chen
Dr. Haifei Zhu
Prof. Dr. Xuefeng Zhou
Prof. Dr. Chaoqun Wang
Prof. Dr. Qiang Li
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • mechanisms for climbing robots
  • material innovations
  • control systems
  • sensing and perception
  • energy efficiency
  • AI and machine learning in climbing
  • applications and case studies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 5891 KiB  
Article
Multi-Indicator Heuristic Evaluation-Based Rapidly Exploring Random Tree Algorithm for Robot Path Planning in Complex Environments
by Wenqiang Wu, Chuixin Kong, Zhongmin Xiao, Qianping Huang, Mingfeng Yu and Zhiye Ren
Machines 2025, 13(4), 274; https://doi.org/10.3390/machines13040274 - 26 Mar 2025
Viewed by 205
Abstract
This paper introduces a multi-indicator heuristic evaluation-based rapidly exploring random tree (MIHE-RRT) algorithm to address the key challenges of robot path planning in complex environments. The core innovation lies in a novel dual optimization framework that combines Hammersley sequence sampling with a comprehensive [...] Read more.
This paper introduces a multi-indicator heuristic evaluation-based rapidly exploring random tree (MIHE-RRT) algorithm to address the key challenges of robot path planning in complex environments. The core innovation lies in a novel dual optimization framework that combines Hammersley sequence sampling with a comprehensive multi-indicator heuristic evaluation mechanism. The Hammersley sequence ensures uniform coverage of the configuration space, while the multi-indicator heuristic evaluation mechanism intelligently guides tree expansion through a three-dimensional evaluation system incorporating diversity, distance, and angle values. After generating the initial path, a pruning algorithm removes redundant points to produce an efficient and practical final path. Extensive experimental validation in four different environmental scenarios (semi-enclosed, maze, chaotic, and crowded) demonstrates that MIHE-RRT outperforms RRT (rapidly exploring random tree), IBi-RRT (improved bidirectional rapidly exploring random tree), and HB-RRT (halton biased rapidly exploring random tree) algorithms. Results show significant improvements in planning efficiency (54–88% reduction in execution time), path quality (15–24% shorter paths), and computational resource utilization (77–94% reduction in nodes). These excellent performance metrics not only prove MIHE-RRT’s advantages in complex environments but also make it particularly suitable for practical robot navigation applications requiring reliable and efficient path planning. Full article
(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)
►▼ Show Figures

Figure 1

17 pages, 5812 KiB  
Article
Trajectory Tracking of a Wall-Climbing Cutting Robot Based on Kinematic and PID Joint Optimization
by Xiaoguang Liu, Zhenmin Wang, Jing Wu, Hongmin Wu and Hao Zhang
Machines 2025, 13(3), 229; https://doi.org/10.3390/machines13030229 - 12 Mar 2025
Viewed by 386
Abstract
Cutting is a crucial step in the industrial production process, particularly in the manufacture of large structures. In certain spatial positions, using a mobile robot, especially a wall-climbing robot (WCR) with adsorption function, is essential for carrying cutting torches to cut large steel [...] Read more.
Cutting is a crucial step in the industrial production process, particularly in the manufacture of large structures. In certain spatial positions, using a mobile robot, especially a wall-climbing robot (WCR) with adsorption function, is essential for carrying cutting torches to cut large steel components. The cutting quality directly impacts the overall manufacturing quality. Therefore, effectively tracking the cutting trajectory of wall-climbing cutting robots is very important. This study proposes a controller based on a kinematic model and PID optimization. The controller is designed to manage the robot’s kinematic trajectory, including the torch slider, through the kinematic modeling of the wall-climbing cutting robot (WCCR). The stability of the control law is proven using the Lyapunov function, which controls the linear and angular velocities of the WCCR and the motion speed of the cross slider. Simulations verify that the control law performs well in tracking both straight-line and circular trajectories. The impact of different control law parameters on straight-line trajectory tracking is also compared. By introducing PID optimization control, the controller’s anti-interference capabilities are enhanced, addressing the issue of motion velocity fluctuation when the WCCR tracks curved trajectories. The simulation and experiment results demonstrate the effectiveness of the proposed controller. Full article
(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)
►▼ Show Figures

Figure 1

13 pages, 10799 KiB  
Article
Development of a Bicycle-like Magnetic-Wheeled Climbing Robot with Adaptive Plane-Transition Capabilities
by Yongjian Bu, Lide Dun, Yongtao Deng, Bingdong Jiang, Aihua Jiang and Haifei Zhu
Machines 2025, 13(2), 167; https://doi.org/10.3390/machines13020167 - 19 Feb 2025
Cited by 1 | Viewed by 399
Abstract
Although robots are increasingly expected to perform inspection tasks in three-dimensional ferromagnetic structural environments, magnetic-wheeled climbing robots face significant challenges in overcoming obstacles and transiting between planes. In this paper, we propose a novel bicycle-like magnetic-wheeled climbing robot, named BiMagBot, featuring two magnetic [...] Read more.
Although robots are increasingly expected to perform inspection tasks in three-dimensional ferromagnetic structural environments, magnetic-wheeled climbing robots face significant challenges in overcoming obstacles and transiting between planes. In this paper, we propose a novel bicycle-like magnetic-wheeled climbing robot, named BiMagBot, featuring two magnetic wheels that allow the adaptive adjustment of magnetic adhesion without the need for active control. The front wheel incorporates an arc tentacle mechanism that rotates a ring magnet to adjust the magnetic adhesion, while the rear wheel uses an eccentric shaft-hole design to facilitate a smooth transition of magnetic adhesion between surfaces. The magnetic forces acting on both wheels during transitions through concave corners were analyzed and discussed via simulations to elucidate the underlying principles. A prototype of the robot was developed and tested experimentally. The results show that the front and rear wheels can adjust the magnetic adhesion during the transition of corners with angles ranging from 90° to 315°. The robot only weighs 1.6 kg, but it can carry a weight of 2 kg with a speed of 0.9 m/s to transit across concave corners, demonstrating comprehensive capabilities in plane transition, ease of control, and load capacity. Full article
(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)
►▼ Show Figures

Figure 1

18 pages, 2083 KiB  
Article
Topology-Aware Efficient Path Planning in Dynamic Environments
by Haoning Zhao, Jiamin Guo, Chaoqun Wang, Xuewen Rong and Yibin Li
Machines 2025, 13(1), 14; https://doi.org/10.3390/machines13010014 - 29 Dec 2024
Viewed by 869
Abstract
This study presents a path-planning approach toward efficient obstacle avoidance in dynamic environments. The developed approach features the awareness of the topological structure of the dynamic environment at a planning instant. It is achieved by employing a homology class path planner to generate [...] Read more.
This study presents a path-planning approach toward efficient obstacle avoidance in dynamic environments. The developed approach features the awareness of the topological structure of the dynamic environment at a planning instant. It is achieved by employing a homology class path planner to generate a set of non-homotopy global paths. The global paths are cast into tree structures separately and optimized by the developed sampling-based path-planning methods. This mechanism can adaptively adjust the optimizing step size according to the change in the dynamic environment, and the sampling module uses the Gaussian Mixture Model (GMM) Optimizer to control the sampling space. The approach seeks the globally optimal path as it maintains and optimizes homology classes of admissible candidate paths of distinctive topologies in parallel. We conduct various experiments in dynamic environments to verify the developed method’s effectiveness and efficiency. It is demonstrated that the developed method can perform better than the state of the art. Full article
(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop