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Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 15349

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Special Issue Editors

Prof. Dr. Alexander Ferrein

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Guest Editor

MASCOR Institute, FH Aachen University of Applied Sciences, Eupener Str. 70, 52066 Aachen, Germany
Interests: AI; robotics; autonomous systems; high-level control
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Michael Reke

E-Mail Website
Guest Editor

MASCOR Institute, FH Aachen University of Applied Sciences, Hohenstaufenallee 10, 52064 Aachen, Germany
Interests: autonomous driving; automotive electronics and software
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For successful mission deployment, robots and autonomous vehicles need robust and safe navigation and path planning algorithms that can cope with unforeseen obstacles and changing environment conditions. While a rich body of work on navigation and path planning methods exists, with this Special Issue we want to turn our attention towards the sensors that those systems operating in real-world environments need to be equipped with to provide the required robust and safe navigation and path planning capabilities. On the sensor hand, many new developments in solid-state LiDAR sensors, 4D radars and high-speed cameras, among others, have recently been observed. Concerning data processing there are discussions about early fusion approaches to combine sensors at the raw data level (e.g., for object detection). Additionally, multiple sensors are arranged around robots and vehicles to realize 360° perception, but also to guarantee stable localization under all conditions. Furthermore, the ideal sensor combination and data evaluation technology to ensure functional safety is still under discussion.
How do these topics advance the field of navigating autonomous systems?

We solicit contributions addressing topics including but not limited to:

Novel sensor technologies;
Examples of real-world applications with focus on the used sensor technologies;
Novel navigation/path planning approaches;
Novel data evaluation and sensor fusion algorithms and technologies;
Examples of applied sensor and data processing architectures for object detection, localization and safety purposes.

Prof. Dr. Alexander Ferrein
Prof. Dr. Michael Reke
Guest Editors

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Keywords

sensors technology
autonomous vehicles
intelligent systems
sensor/data processing
sensor fusion
path planning
navigation

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Published Papers (9 papers)

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Research

21 pages, 3270 KiB

Open AccessArticle

Spatio-Temporal Joint Trajectory Planning for Autonomous Vehicles Based on Improved Constrained Iterative LQR

by Qin Li, Hongwen He, Manjiang Hu and Yong Wang

Sensors 2025, 25(2), 512; https://doi.org/10.3390/s25020512 - 17 Jan 2025

Viewed by 1045

Abstract

With advancements in autonomous driving technology, the coupling of spatial paths and temporal speeds in complex scenarios becomes increasingly significant. Traditional sequential decoupling methods for trajectory planning are no longer sufficient, emphasizing the need for spatio-temporal joint trajectory planning. The Constrained Iterative LQR (CILQR), based on the Iterative LQR (ILQR) method, shows obvious potential but faces challenges in computational efficiency and scenario adaptability. This paper introduces three key improvements: a segmented barrier function truncation strategy with dynamic relaxation factors to enhance stability, an adaptive weight parameter adjustment method for acceleration and curvature planning, and the integration of the hybrid A* algorithm to optimize the initial reference trajectory and improve iterative efficiency. The improved CILQR method is validated through simulations and real-vehicle tests, demonstrating substantial improvements in human-like driving performance, traffic efficiency improvement, and real-time performance while maintaining comfortable driving. The experiment’s results demonstrate a significant increase in human-like driving indicators by 16.35% and a 12.65% average increase in traffic efficiency, reducing computation time by 39.29%. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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23 pages, 12001 KiB

Open AccessArticle

Enhancing Off-Road Topography Estimation by Fusing LIDAR and Stereo Camera Data with Interpolated Ground Plane

by Gustav Sten, Lei Feng and Björn Möller

Sensors 2025, 25(2), 509; https://doi.org/10.3390/s25020509 - 16 Jan 2025

Viewed by 843

Abstract

Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage but lower accuracy. LIDARs, on the other hand, have higher accuracy and longer range but much less coverage. LIDARs are also more expensive. The research question examines whether incorporating LIDARs can significantly improve stereo camera accuracy. Current sensor fusion methods use LIDARs’ raw measurements directly; thus, the improvement in estimation accuracy is limited to only LIDAR-scanned locations The main contribution of our new method is to construct a reference ground plane through the interpolation of LIDAR data so that the interpolated maps have similar coverage as the stereo camera’s point cloud. The interpolated maps are fused with the stereo camera point cloud via Kalman filters to improve a larger section of the topography map. The method is tested in three environments: controlled indoor, semi-controlled outdoor, and unstructured terrain. Compared to the existing method without LIDAR interpolation, the proposed approach reduces average error by 40% in the controlled environment and 67% in the semi-controlled environment, while maintaining large coverage. The unstructured environment evaluation confirms its corrective impact. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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17 pages, 892 KiB

Open AccessArticle

A Smooth Global Path Planning Method for Unmanned Surface Vehicles Using a Novel Combination of Rapidly Exploring Random Tree and Bézier Curves

by Betül Z. Türkkol, Nihal Altuntaş and Sırma Çekirdek Yavuz

Sensors 2024, 24(24), 8145; https://doi.org/10.3390/s24248145 - 20 Dec 2024

Cited by 2 | Viewed by 1010

Abstract

Developing autonomous navigation techniques for surface vehicles remains an important research area, and accurate global path planning is essential. For mobile robots—particularly for Unmanned Surface Vehicles (USVs)—a key challenge is ensuring that sharp turns and sharp breaks are avoided. Therefore, global path planning must not only calculate the shortest path but also provide smoothness. Bézier Curves are one of the main methods used for smoothing paths in the literature. Some studies have focused on turns alone; however, continuous path smoothness across the entire trajectory enhances navigational quality. Contrary to similar studies, we applied Bézier Curves whose control polygon is defined by an RRT path and thus avoided a multi-objective formulation. In the final stage of our approach, we proposed a control point reduction method in order to decrease the time complexity without affecting the feasibility of the path. Our experimental results suggest significant improvements for multiple map sizes, in terms of path smoothness. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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34 pages, 11952 KiB

Open AccessFeature PaperArticle

Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling

by Mohamed Reda, Ahmed Onsy, Amira Y. Haikal and Ali Ghanbari

Sensors 2024, 24(14), 4650; https://doi.org/10.3390/s24144650 - 17 Jul 2024

Cited by 3 | Viewed by 1869

Abstract

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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36 pages, 34495 KiB

Open AccessArticle

A Novel 3D Reconstruction Sensor Using a Diving Lamp and a Camera for Underwater Cave Exploration

by Quentin Massone, Sébastien Druon and Jean Triboulet

Sensors 2024, 24(12), 4024; https://doi.org/10.3390/s24124024 - 20 Jun 2024

Cited by 1 | Viewed by 1284

Abstract

Aquifer karstic structures, due to their complex nature, present significant challenges in accurately mapping their intricate features. Traditional methods often rely on invasive techniques or sophisticated equipment, limiting accessibility and feasibility. In this paper, a new approach is proposed for a non-invasive, low-cost 3D reconstruction using a camera that observes the light projection of a simple diving lamp. The method capitalizes on the principles of structured light, leveraging the projection of light contours onto the karstic surfaces. By capturing the resultant light patterns with a camera, three-dimensional representations of the structures are reconstructed. The simplicity and portability of the equipment required make this method highly versatile, enabling deployment in diverse underwater environments. This approach is validated through extensive field experiments conducted in various aquifer karstic settings. The results demonstrate the efficacy of this method in accurately delineating intricate karstic features with remarkable detail and resolution. Furthermore, the non-destructive nature of this technique minimizes disturbance to delicate aquatic ecosystems while providing valuable insights into the subterranean landscape. This innovative methodology not only offers a cost-effective and non-invasive means of mapping aquifer karstic structures but also opens avenues for comprehensive environmental monitoring and resource management. Its potential applications span hydrogeological studies, environmental conservation efforts, and sustainable water resource management practices in karstic terrains worldwide. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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17 pages, 4788 KiB

Open AccessArticle

Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection

by Jinwei Zhang, Xijing Zhu and Jing Li

Sensors 2024, 24(4), 1104; https://doi.org/10.3390/s24041104 - 8 Feb 2024

Cited by 14 | Viewed by 1813

Abstract

Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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28 pages, 12227 KiB

Open AccessArticle

Research on Trajectory-Tracking Control System of Tracked Wall-Climbing Robots

by Haoyan Zhang, Jiaqi Wu, Yang An, Pengshu Xie and Da Cui

Sensors 2024, 24(1), 144; https://doi.org/10.3390/s24010144 - 27 Dec 2023

Cited by 2 | Viewed by 1591

Abstract

Different from the vehicles and robots that move on the ground, complex and nonlinear track–wall interactions bring considerable difficulties to the accurate control of tracked wall-climbing robots due to the effect of gravity and adsorption. In this article, the authors propose a trajectory-tracking control system for tracked wall-climbing robots based on the fuzzy logic computed-torque control (FLCT) method. A key element in the proposed control strategy is to consider the adsorption force and gravity compensation based on the dynamic model. Validated via numerical simulations and experiments, the results show that the proposed controller can track the reference trajectory quickly, accurately and stably. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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44 pages, 16432 KiB

Open AccessArticle

Viewpoint Planning for Range Sensors Using Feature Cluster Constrained Spaces for Robot Vision Systems

by Alejandro Magaña, Michiel Vlaeyen, Han Haitjema, Philipp Bauer, Benedikt Schmucker and Gunther Reinhart

Sensors 2023, 23(18), 7964; https://doi.org/10.3390/s23187964 - 18 Sep 2023

Cited by 5 | Viewed by 1893

Abstract

The efficient computation of viewpoints for solving vision tasks comprising multi-features (regions of interest) represents a common challenge that any robot vision system (RVS) using range sensors faces. The characterization of valid and robust viewpoints is even more complex within real applications that require the consideration of various system constraints and model uncertainties. Hence, to address some of the challenges, our previous work outlined the computation of valid viewpoints as a geometrical problem and proposed feature-based constrained spaces (

C

-spaces) to tackle this problem efficiently for acquiring one feature. The present paper extends the concept of

C

-spaces to consider multi-feature problems using feature cluster constrained spaces (

^{G} C

-spaces). A

^{G} C

-space represents a closed-form, geometrical solution that provides an infinite set of valid viewpoints for acquiring a cluster of features satisfying diverse viewpoint constraints. Furthermore, the current study outlines a generic viewpoint planning strategy based on

^{G} C

-spaces for solving vision tasks comprising multi-feature scenarios effectively and efficiently. The applicability of the proposed framework is validated on two different industrial vision systems used for dimensional metrology tasks. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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14 pages, 3134 KiB

Open AccessArticle

Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy

by Yuhan Li, Ruizhi Ruan, Zupeng Zhou, Anqing Sun and Xiaonan Luo

Sensors 2023, 23(9), 4398; https://doi.org/10.3390/s23094398 - 29 Apr 2023

Cited by 8 | Viewed by 2708

Abstract

This paper presents a novel method for the dynamic positioning of an unmanned underwater vehicle (UUV) with unknown trajectories based on an autonomous tracking buoy (PUVV-ATB) that indirectly positions the UUV using ultra-short baseline measurements. The method employs a spatial location geometric model and divides the positioning process into four steps, including data preprocessing to detect geometric errors and apply mean filtering, direction capture, position tracking, and position synchronization. To achieve these steps, a new adaptive tracking control algorithm is proposed that does not require trajectory prediction and is applied to the last three steps. The algorithm is deployed to the buoy for tracking simulation and sea trial experiments, and the results are compared with those of a model predictive control algorithm. The autonomous tracking buoy based on the adaptive tracking control algorithm runs more stably and can better complete the precise tracking task for the UUV with a positioning error of less than 10 cm. This method breaks the premise of trajectory prediction based on traditional tracking control algorithms, providing a new direction for further research on UUV localization. Furthermore, the conclusion of this paper has important reference value for other research and application fields related to UUV. Full article

(This article belongs to the Special Issue Sensors for Path Planning and Navigation in Robotics and Autonomous Vehicles)

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