Flight Path Planning of Multiple UAVs for Robust Localization near Infrastructure Facilities

Maeda, Kazuyuki; Doki, Shinji; Funabora, Yuki; Doki, Kae

doi:10.1109/iecon.2018.8592710

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…The KITTI dataset enables the use of either LiDAR or RGB as input, or both. As per Lahoud, methods utilizing LiDAR information tend to outperform those relying solely on RGB, given that the necessary 3D information is not contained in RGB, rendering it incapable of accurately placing the bounding box [112,113]. In contrast, Monoflex [114] falls short of the transformer-based architecture MonoDETR [115].…”

Section: D Object Detectionmentioning

confidence: 99%

Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing

Ding

Sun

et al. 2023

Robotics

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In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field.

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Section: D Object Detectionmentioning

confidence: 99%

Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing

Ding

Sun

et al. 2023

Robotics

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“…In Ref. [23], the method achieves higher accuracy by proposing a deep reinforcement learning algorithm. However, due to the high computational cost of the deep reinforcement learning algorithm, such method is not suitable for UAVs equipped with limited computing equipment and power source.…”

Section: Related Workmentioning

confidence: 99%

“…They can achieve high localization accuracy but will heavily increase energy consumption while degrading time efficiency. Multiple UAVs can enhance the searching range by collaborative search and localization [21,22] .…”

Section: Related Workmentioning

confidence: 99%

Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

Wu¹,

Gu²

2022

Chinese J of Electronics

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Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles (UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.

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“…As UAVs operate in the air, multipath interference is largely weakened. Thus, UAVs are expected to achieve efficient interference source localization [7]. What's more, UAVs can carry equipment like electronic scanning antenna along with other sensors, enabling it to handle the challenging localization task.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Interference Source Localization: A Multimodal Q-Learning Approach

2019

IEEE Access

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Localization problem is a significant component of the Internet of Things (IoT) and interference source localization is of great importance in the context of spectrum monitoring and management. However, it remains challenging to quickly but accurately locate an interference source from the distance, especially when little is known about the interference source. To handle this problem, a single learning algorithm can be exploited to search and locate the interference source. However, it is varying dynamics in varying environments that can make the design of such a learning algorithm intractable. In our study, we employ an unmanned aerial vehicle (UAV) to realize the localization. Moreover, a novel multimodal Q-learning framework along with its algorithm is proposed, and the framework combines pattern recognition with Q-learning. The proposed learning architecture can adjust the parameters of Q-learning algorithm dynamically based on the changing environments so as to achieve better detection precision, longer localization distance and shorter searching time. The simulation verifies multimodal Q-learning algorithm's performance on interference source localization along with its capability of adapting to environmental change. The simulation results confirm the proposed concept of multimodal Q-learning. It is shown that the multimodal Q-learning based localization algorithms can outperform various baselines in terms of both accuracy and detection distance. The searching time consumed by the UAV is also largely reduced. This observation indicates that the capability of environmental adaption introduced by the proposed multimodal framework can benefit the Q-learning algorithms.

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Flight Path Planning of Multiple UAVs for Robust Localization near Infrastructure Facilities

Cited by 10 publications

References 19 publications

Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing

Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing

Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

UAV-Based Interference Source Localization: A Multimodal Q-Learning Approach

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