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Advances in Sensing, Control and Path Planning for Robotic Systems
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Advances in Sensing, Control and Path Planning for Robotic Systems

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 5068

Special Issue Editors

Dr. Rafal Szczepanski

E-Mail Website
Guest Editor
Institute of Engineering and Technology, Faculty of Physics Astronomy and Informatics, Nicolaus Copernicus University, Wilenska 7, 87-100 Torun, Poland
Interests: automatic control; adaptive controllers; electric motors; path planning algorithms; nature-inspired optimization algorithms; autonomous vehicles
Dr. Erfu Yang

E-Mail Website
Guest Editor
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK
Interests: robotics and autonomous systems; mechatronics and automation; data analytics; intelligent control; computational intelligence; digital manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced sensing, control and path-planning algorithms for robotic systems have gained much popularity in recent years. For example, in advanced manufacturing, specialists have the ability to increase manufacturing productivity and energy efficiency through the use of these advanced algorithms. However, improved performance and safety requirements are causing a steady increase in the complexity of robotic control and path planning problems. Moreover, on-board sensing systems require advanced sensor-fusion and signal filtering methods to provide high-quality perceptions.

This Special Issue, therefore, aims to collect original research and review articles on recent advances in technologies, solutions, applications, and challenges in the field of robotic systems. Authors are invited to submit high-quality papers on topics including, but not limited to, the following:

  • Global path planning algorithms;
  • Local path planning algorithms;
  • Localization algorithms;
  • Space exploration;
  • Task scheduling algorithms;
  • Sensor-fusion methods;
  • Advanced filtering;
  • Autonomous robots in unknown environment;
  • Multi-agent robotic systems;
  • Human–robot interaction;
  • Optimization of robotic systems;
  • Examples of real-world applications of autonomous robots.

Dr. Rafal Szczepanski
Dr. Erfu Yang
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

  • path planning algorithms
  • space exploration
  • multi-agent systems, nature-inspired optimization algorithms
  • autonomous vehicle
  • unmanned vehicle
  • mobile robot
  • sensor-fusion
  • advanced filtering

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Published Papers (5 papers)

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Research

21 pages, 5415 KiB  
Article
FHQ-RRT*: An Improved Path Planning Algorithm for Mobile Robots to Acquire High-Quality Paths Faster
by Xingxiang Dong, Yujun Wang, Can Fang, Kemeng Ran and Guohui Liu
Sensors 2025, 25(7), 2189; https://doi.org/10.3390/s25072189 - 30 Mar 2025
Viewed by 577
Abstract
The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths [...] Read more.
The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, termed Faster High-Quality RRT*(FHQ-RRT*). The proposed algorithm enhances the exploration efficiency and path quality of mobile robots through three key innovations: First, a dynamic sparse sampling strategy that adaptively adjusts the sampling density according to the growth rate of the random tree, thereby increasing the algorithm’s growth speed while maintaining adaptability to complex environments. Second, a new node creation method that combines the bisection method, triangle inequality, and the concept of KeyPoints to reduce the cost of creating new nodes. Third, a focused rewiring strategy that restricts the rewiring operation to valuable regions, thereby improving rewiring efficiency. The performance of FHQ-RRT* was validated in four simulation maps and compared with other algorithms. In all validated maps, FHQ-RRT* consistently achieved the lowest path cost. Regarding time cost, FHQ-RRT* reduced the planning time by over 40% in the circular-obstacle map, 77% in the simple maze map, 56% in the complex maze map, and 50% in the narrow map. The simulation results show that FHQ-RRT* can rapidly generate high-quality paths faster than other algorithms. Full article
(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)
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14 pages, 974 KiB  
Article
N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning
by Juliana Manrique-Cordoba, Miguel Ángel de la Casa-Lillo and José María Sabater-Navarro
Sensors 2025, 25(7), 2145; https://doi.org/10.3390/s25072145 - 28 Mar 2025
Viewed by 266
Abstract
This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based [...] Read more.
This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm’s capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces. Full article
(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)
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19 pages, 50560 KiB  
Article
Garment Recognition and Reconstruction Using Object Simultaneous Localization and Mapping
by Yilin Zhang and Koichi Hashimoto
Sensors 2024, 24(23), 7622; https://doi.org/10.3390/s24237622 - 28 Nov 2024
Cited by 1 | Viewed by 793
Abstract
The integration of robotics in the garment industry remains relatively limited, primarily due to the challenges in the highly deformable nature of garments. The objective of this study is thus to explore a vision-based garment recognition and environment reconstruction model to facilitate the [...] Read more.
The integration of robotics in the garment industry remains relatively limited, primarily due to the challenges in the highly deformable nature of garments. The objective of this study is thus to explore a vision-based garment recognition and environment reconstruction model to facilitate the application of robots in garment processing. Object SLAM (Simultaneous Localization and Mapping) was employed as the core methodology for real-time mapping and tracking. To enable garment detection and reconstruction, two datasets were created: a 2D garment image dataset for instance segmentation model training and a synthetic 3D mesh garment dataset to enhance the DeepSDF (Signed Distance Function) model for generative garment reconstruction. In addition to garment detection, the SLAM system was extended to identify and reconstruct environmental planes, using the CAPE (Cylinder and Plane Extraction) model. The implementation was tested using an Intel Realsense® camera, demonstrating the feasibility of simultaneous garment and plane detection and reconstruction. This study shows improved performance in garment recognition with the 2D instance segmentation models and an enhanced understanding of garment shapes and structures with the DeepSDF model. The integration of CAPE plane detection with SLAM allows for more robust environment reconstruction that is capable of handling multiple objects. The implementation and evaluation of the system highlight its potential for enhancing automation and efficiency in the garment processing industry. Full article
(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)
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11 pages, 4182 KiB  
Article
Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization
by Sri Harsha Turlapati, Van Pho Nguyen, Juhi Gurnani, Mohammad Zaidi Bin Ariffin, Sreekanth Kana, Alvin Hong Yee Wong, Boon Siew Han and Domenico Campolo
Sensors 2024, 24(23), 7465; https://doi.org/10.3390/s24237465 - 22 Nov 2024
Viewed by 1043
Abstract
Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot’s intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction [...] Read more.
Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot’s intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured. In this work, we propose an approach for identifying and predicting the intrinsic dynamics using linear regression with non-linear radial basis functions. Then, we validate this regression on a wheel-bearing turning task, in which its friction is a measure of quality, and thus must be accurately measured. The results showed that the bearing torque measured by the joint 7 torque sensor was within an RMS error of 11% of the torque measured by the external force/torque sensor. This error is much lower than that before our proposed model in compensating the intrinsic dynamics of the robot arm. Full article
(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)
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18 pages, 1785 KiB  
Article
Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot
by Rafal Szczepanski, Krystian Erwinski, Mateusz Tejer and Dominika Daab
Sensors 2024, 24(16), 5332; https://doi.org/10.3390/s24165332 - 17 Aug 2024
Cited by 1 | Viewed by 1658
Abstract
This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are [...] Read more.
This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm. Full article
(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)
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