Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advances in Sensing, Control and Path Planning for Robotic Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (25 March 2026) | Viewed by 19561

Share This Special Issue

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 250 words) can be sent to the Editorial Office for assessment.

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

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 (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

38 pages, 3132 KB

Open AccessArticle

Lightweight Semantic-Aware Route Planning on Edge Hardware for Indoor Mobile Robots: Monocular Camera–2D LiDAR Fusion with Penalty-Weighted Nav2 Route Server Replanning

by Bogdan Felician Abaza, Andrei-Alexandru Staicu and Cristian Vasile Doicin

Sensors 2026, 26(7), 2232; https://doi.org/10.3390/s26072232 - 4 Apr 2026

Viewed by 1519

Abstract

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)

► Show Figures

Figure 1

17 pages, 5360 KB

Open AccessArticle

Experimental Validation of the Direct Kinematics of a Passive Stewart-Gough Platform with Modified Cardan Joints Using Integrated Absolute Linear Encoders

by Martin Bem, Aleš Ude and Bojan Nemec

Sensors 2026, 26(3), 771; https://doi.org/10.3390/s26030771 - 23 Jan 2026

Viewed by 581

Abstract

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization-based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications. Full article

(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)

► Show Figures

Figure 1

33 pages, 4138 KB

Open AccessArticle

Collaborative Swarm Robotics for Object Transport via Caging

by Nadia Nedjah, Karen da Silva Cardoso and Luiza de Macedo Mourelle

Sensors 2025, 25(16), 5063; https://doi.org/10.3390/s25165063 - 14 Aug 2025

Viewed by 1662

Abstract

In swarm robotics, collective transport refers to the cooperative movement of a large object by multiple small robots, each with limited individual capabilities such as sensing, mobility, and communication. When working together, however, these simple agents can achieve complex tasks. This study explores a collective transport method based on the caging approach, which involves surrounding the object in a way that restricts its movement while still allowing limited motion, effectively preventing escape from the robot formation. The proposed approach is structured into four main phases: locating the object, recruiting additional robots, forming an initial cage around the object, and finally, performing the transportation. The method is tested using simulations in the CoppeliaSim environment, employing a team of Khepera-III robots. Performance metrics include execution time for the search and recruitment phases, and both execution time and trajectory accuracy, via a normalized error, for the transport phase. To further validate the method, a comparison is made between the caging-based strategy and a traditional pushing strategy. Full article

(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)

► Show Figures

Figure 1

21 pages, 5415 KB

Open AccessArticle

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

Cited by 10 | Viewed by 3300

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 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)

► Show Figures

Figure 1

14 pages, 974 KB

Open AccessArticle

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

Cited by 5 | Viewed by 1136

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 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)

► Show Figures

Figure 1

19 pages, 50560 KB

Open AccessArticle

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 1835

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 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)

► Show Figures

Figure 1

11 pages, 4182 KB

Open AccessArticle

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

Cited by 7 | Viewed by 2766

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 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)

► Show Figures

Figure 1

18 pages, 1785 KB

Open AccessArticle

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 6 | Viewed by 3784

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

19 pages, 1994 KB

Open AccessReview

Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects

by Zexu Dong, Jiashu Zheng, Chenxuan Guo, Fangming Zhao, Yijie Chu and Xiaojun Chen

Sensors 2026, 26(9), 2852; https://doi.org/10.3390/s26092852 - 2 May 2026

Viewed by 1679

Abstract

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article

(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)

► Show Figures

Journal Menu

Journal Browser

Advances in Sensing, Control and Path Planning for Robotic Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI