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Special Issue "Aerial Robotics: Navigation and Path Planning"

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

Deadline for manuscript submissions: 31 March 2023 | Viewed by 10479

Special Issue Editors

Dr. Hriday Bavle
E-Mail Website
Guest Editor
Postdoctoral Research Associate, Automation and Robotics Research Group (ARG), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: SLAM; situational awareness; semantic perception; aerial robots; mobile robots; autonomous architectures; deep learning applied to robots
Dr. Jose Luis Sanchez-Lopez
E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: machine learning; computer vision; motion control; slam; multi-robot systems; state estimation; situational awareness; aerial robots; trajectory planning; software architectures
Prof. Dr. Holger Voos
E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: aerial robots; soft robotics; space robotics; robot control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last two decades have witnessed a drastic maturing and widespread availability of the components and technologies required for aerial robotics. This has led to an unprecedented level of increase in the useful and challenging applications of aerial robotics, which include filming, precision agriculture, leakage detection in different pipe networks, electrical power-grid monitoring, environment monitoring, traffic monitoring, and wildlife monitoring. Aerial robots must be equipped with reliable positioning and actuation equipment so as to be capable of controlled flight, and this constitutes a nontrivial requirement prior to conducting research or development in this field.

This Special Issue of Sensors will focus on aerial robots’ navigation and path planning. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Aerial Robotics;
  • SLAM;
  • Navigation;
  • Path Planning;
  • Deep Learning
  • Global Position System;
  • Unmanned Aerial Vehicle;
  • Precision agriculture;
  • Aerial manipulation

Dr. Hriday Bavle
Dr. Jose Luis Sanchez-Lopez
Prof. Dr. Holger Voos
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 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

  • Aerial Robotics
  • SLAM
  • Navigation
  • Path Planning
  • Deep Learning
  • Global Position System
  • Unmanned Aerial Vehicle
  • Precision agriculture
  • Aerial manipulation

Published Papers (9 papers)

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Research

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Article
Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results
Sensors 2023, 23(2), 664; https://doi.org/10.3390/s23020664 - 06 Jan 2023
Viewed by 518
Abstract
UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this [...] Read more.
UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
Resolution and Frequency Effects on UAVs Semi-Direct Visual-Inertial Odometry (SVO) for Warehouse Logistics
Sensors 2022, 22(24), 9911; https://doi.org/10.3390/s22249911 - 16 Dec 2022
Viewed by 474
Abstract
For the commercial sector, warehouses are becoming increasingly vital. Constant efforts are in progress to increase the efficiency of these facilities while reducing costs. The inventory part of the goods is a time-consuming task that impacts the company’s revenue. This article presents an [...] Read more.
For the commercial sector, warehouses are becoming increasingly vital. Constant efforts are in progress to increase the efficiency of these facilities while reducing costs. The inventory part of the goods is a time-consuming task that impacts the company’s revenue. This article presents an analysis of the performance of a state-of-the-art, visual-inertial odometry algorithm, SVO Pro Open, when varying the resolution and frequency of video streaming in an industrial environment. To perform efficiently this task, achieving an optimal system in terms of localization accuracy, robustness, and computational cost is necessary. Different resolutions are selected with a constant aspect ratio, and an accurate calibration for each resolution configuration is performed. A stable operating point in terms of robustness, accuracy of localization, and CPU utilization is found and the trends obtained are studied. To keep the system robust against sudden divergence, the feature loss factor extracted from optical sensors is analyzed. Innovative trends and translation errors on the order of a few tens of centimeters are achieved, allowing the system to navigate safely in the warehouse. The best result is obtained at a resolution of 636 × 600 px, where the localization errors (x, y, and z) are all under 0.25 m. In addition, the CPU (Central Processing Unit) usage of the onboard computer is kept below 60%, remaining usable for other relevant onboard processing tasks. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning
Sensors 2022, 22(18), 6737; https://doi.org/10.3390/s22186737 - 06 Sep 2022
Viewed by 589
Abstract
In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate [...] Read more.
In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate the tasks area for each UAV. Then, Independent Deep Reinforcement Learning (IDRL) is used to solve Nash equilibrium to improve UAVs’ collaborations. The proposed reward function is smartly developed to guide UAVs to fly along the path with higher reward value while avoiding the collisions between UAVs during flights. Finally, extensive experiments are carried out to compare our proposed method with other algorithms. Simulation results show that the proposed method can obtain more rewards in the same period of time as other algorithms. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
Performance Analysis of Localization Algorithms for Inspections in 2D and 3D Unstructured Environments Using 3D Laser Sensors and UAVs
Sensors 2022, 22(14), 5122; https://doi.org/10.3390/s22145122 - 07 Jul 2022
Viewed by 808
Abstract
One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to [...] Read more.
One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
Finite-Time Asynchronous Event-Triggered Formation of UAVs with Semi-Markov-Type Topologies
Sensors 2022, 22(12), 4529; https://doi.org/10.3390/s22124529 - 15 Jun 2022
Viewed by 710
Abstract
In this paper, the finite-time formation problem of UAVs is investigated with consideration of semi-Markov-type switching topologies. More precisely, finite-time passivity performance is adopted to overcome the dynamical effect of disturbances. Furthermore, an asynchronous event-triggered communication scheme is proposed for more efficient information [...] Read more.
In this paper, the finite-time formation problem of UAVs is investigated with consideration of semi-Markov-type switching topologies. More precisely, finite-time passivity performance is adopted to overcome the dynamical effect of disturbances. Furthermore, an asynchronous event-triggered communication scheme is proposed for more efficient information exchanges. The mode-dependent formation controllers are designed in terms of the Lyapunov–Krasovskii method, such that the configuration formation can be accomplished. Finally, simulation results are given to demonstrate the validity of the proposed formation approach. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
Sensors 2022, 22(7), 2651; https://doi.org/10.3390/s22072651 - 30 Mar 2022
Viewed by 1956
Abstract
Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and [...] Read more.
Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and degenerate motions. In addition, concerning supervised learning, we can fully leverage video stream data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the translational component. Therefore, we present RAUM-VO, an approach based on a model-free epipolar constraint for frame-to-frame motion estimation (F2F) to adjust the rotation during training and online inference. To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol. Then, we adjust the predicted rotation with the motion estimated by F2F using the 2D matches and initializing the solver with the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy improvement compared to other unsupervised pose networks on the KITTI dataset, while reducing the complexity of other hybrid or traditional approaches and achieving comparable state-of-the-art results. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Article
Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications
Sensors 2022, 22(6), 2297; https://doi.org/10.3390/s22062297 - 16 Mar 2022
Cited by 8 | Viewed by 2428
Abstract
This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on [...] Read more.
This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Review

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Review
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives
Sensors 2023, 23(1), 188; https://doi.org/10.3390/s23010188 - 24 Dec 2022
Viewed by 837
Abstract
Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance [...] Read more.
Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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Review
Visual SLAM: What Are the Current Trends and What to Expect?
Sensors 2022, 22(23), 9297; https://doi.org/10.3390/s22239297 - 29 Nov 2022
Viewed by 1150
Abstract
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are [...] Read more.
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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