Research

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28 pages, 809 KiB

Open AccessArticle

Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem

by Abhishek Nayak and Sivakumar Rathinam

Sensors 2023, 23(14), 6432; https://doi.org/10.3390/s23146432 - 15 Jul 2023

Cited by 1 | Viewed by 1083

This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as the one-in-a-set Dubins mTSP [...] Read more.

This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as the one-in-a-set Dubins mTSP problem (MD-GmTSP), as a mixed-integer linear program (MILP). We then develop heuristic-based search methods for the MD-GmTSP using tour construction algorithms to generate initial feasible solutions relatively fast and then improve on these solutions using variants of the variable neighborhood search (VNS) metaheuristic. Finally, we also explore a graph neural network to implicitly learn policies for the MD-GmTSP using a learning-based approach; specifically, we employ an S-sample batch reinforcement learning method on a shared graph neural network architecture and distributed policy networks to solve the MD-GMTSP. All the proposed algorithms are implemented on modified TSPLIB instances, and the performance of all the proposed algorithms is corroborated. The results show that learning based approaches work well for smaller sized instances, while the VNS based heuristics find the best solutions for larger instances. Full article

(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)

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20 pages, 9943 KiB

Open AccessArticle

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

by Youyu Liu, Wanbao Tao, Shunfang Li, Yi Li and Qijie Wang

Sensors 2023, 23(11), 5172; https://doi.org/10.3390/s23115172 - 29 May 2023

Viewed by 1229

Abstract

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, [...] Read more.

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator. Full article

(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)

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30 pages, 4483 KiB

Open AccessArticle

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

by Prince E. Kuevor, Maani Ghaffari, Ella M. Atkins and James W. Cutler

Sensors 2023, 23(8), 3897; https://doi.org/10.3390/s23083897 - 11 Apr 2023

Cited by 1 | Viewed by 1529

Abstract

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression [...] Read more.

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV’s electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV’s flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle’s PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps’ accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors. Full article

(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)

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37 pages, 13187 KiB

Open AccessArticle

Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results

by Markus Zwick, Matthias Gerdts and Peter Stütz

Sensors 2023, 23(2), 664; https://doi.org/10.3390/s23020664 - 06 Jan 2023

Cited by 3 | Viewed by 2342

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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18 pages, 5930 KiB

Open AccessArticle

Resolution and Frequency Effects on UAVs Semi-Direct Visual-Inertial Odometry (SVO) for Warehouse Logistics

by Simone Godio, Adrian Carrio, Giorgio Guglieri and Fabio Dovis

Sensors 2022, 22(24), 9911; https://doi.org/10.3390/s22249911 - 16 Dec 2022

Viewed by 1614

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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18 pages, 2994 KiB

Open AccessArticle

Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning

by Mingsheng Gao and Xiaoxuan Zhang

Sensors 2022, 22(18), 6737; https://doi.org/10.3390/s22186737 - 06 Sep 2022

Cited by 2 | Viewed by 1713

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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26 pages, 24597 KiB

Open AccessArticle

Performance Analysis of Localization Algorithms for Inspections in 2D and 3D Unstructured Environments Using 3D Laser Sensors and UAVs

by Paul Espinosa Peralta, Marco Andrés Luna, Paloma de la Puente, Pascual Campoy, Hriday Bavle, Adrián Carrio and Christyan Cruz Ulloa

Sensors 2022, 22(14), 5122; https://doi.org/10.3390/s22145122 - 07 Jul 2022

Cited by 2 | Viewed by 1936

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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19 pages, 527 KiB

Open AccessArticle

Finite-Time Asynchronous Event-Triggered Formation of UAVs with Semi-Markov-Type Topologies

by Chao Ma, Suiwu Zheng, Tao Xu and Yidao Ji

Sensors 2022, 22(12), 4529; https://doi.org/10.3390/s22124529 - 15 Jun 2022

Cited by 3 | Viewed by 1251

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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19 pages, 2171 KiB

Open AccessArticle

RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry

by Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez and Holger Voos

Sensors 2022, 22(7), 2651; https://doi.org/10.3390/s22072651 - 30 Mar 2022

Cited by 3 | Viewed by 3032

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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25 pages, 16069 KiB

Open AccessArticle

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

by Marco Andrés Luna, Mohammad Sadeq Ale Isaac, Ahmed Refaat Ragab, Pascual Campoy, Pablo Flores Peña and Martin Molina

Sensors 2022, 22(6), 2297; https://doi.org/10.3390/s22062297 - 16 Mar 2022

Cited by 26 | Viewed by 5761

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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35 pages, 5910 KiB

Open AccessReview

From SLAM to Situational Awareness: Challenges and Survey

by Hriday Bavle, Jose Luis Sanchez-Lopez, Claudio Cimarelli, Ali Tourani and Holger Voos

Sensors 2023, 23(10), 4849; https://doi.org/10.3390/s23104849 - 17 May 2023

Cited by 6 | Viewed by 3968

Abstract

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness [...] Read more.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions. Full article

(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)

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30 pages, 3034 KiB

Open AccessReview

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

by Meisam Kabiri, Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez and Holger Voos

Sensors 2023, 23(1), 188; https://doi.org/10.3390/s23010188 - 24 Dec 2022

Cited by 6 | Viewed by 3197

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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29 pages, 9493 KiB

Open AccessReview

Visual SLAM: What Are the Current Trends and What to Expect?

by Ali Tourani, Hriday Bavle, Jose Luis Sanchez-Lopez and Holger Voos

Sensors 2022, 22(23), 9297; https://doi.org/10.3390/s22239297 - 29 Nov 2022

Cited by 26 | Viewed by 13093

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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