Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

Pedro, Dário; Matos-Carvalho, J. P.; Fonseca, José; Mora, André

doi:10.3390/rs13132643

Cited by 17 publications

(7 citation statements)

References 49 publications

(55 reference statements)

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“…To avoid collisions involving a moving object, such as a tossed ball, [60] proposes an approach that employs Neural Network Pipeline (NNP) to forecast crashes and an Object Trajectory Estimation (OTE) technique that leverages optical flow. The biggest issue in related research is the drone's poor speed.…”

Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…The FCM clustering method was employed for determining the cluster, rules are generated for the FIS via expert assessment, and alternate UAV techniques were prioritized. Pedro et al [16] proposed a safety problem i.e., frequently overlooked because of a lack of solutions and technology to resolve it: collision with non-stationary object. A technique is determined that uses DL technique to resolve the computational problem of real-world collision avoidance with dynamic objects through offthe-shelf commercial vision sensor.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Hilal¹,

Elkamchouchi²,

Eltahir³

et al. 2023

Computer Systems Science and Engineering

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Recently, unmanned aerial vehicles (UAV) or drones are widely employed for several application areas such as surveillance, disaster management, etc. Since UAVs are limited to energy, efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station (BS). Therefore, clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs. In this aspect, this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system (GTOADL-SCS) technique for UAV networks. The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification. At the initial stage, the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads (CHs) and organize clusters. Besides, the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs, average neighoring distance, and UAV degree. For classification process, the GTOADL-SCS model applies pre-trained densely connected network (DenseNet201) feature extractor with gated recurrent unit (GRU) classifier. For ensuring the enhanced performance of the GTOADL-SCS model, a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio (PDR) of 92.60%.

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“…First, the sigmoid layer decides which input value (i t ) should be updated given, as in (8), then the tanh layer creates a vector of new candidate values (c t ) given, as in (9). At forget gate, ConvLSTM decides which information (f t ) should be forgotten from the cell states, as in (10). Based on the update at the input and forget gate, the old cell state c t−1 updates cell state (c t ) given, as in (11).…”

Section: Recurrent Networkmentioning

confidence: 99%

“…Such predictive cognitive neural networks are often considered the essence of computer vision. They play a critical role in a variety of applications, such as abnormal event detection [1], autonomous driving [2][3][4], intention prediction in robotics [5,6], video coding [7,8], collision avoidance systems [9,10], activity and event prediction [11,12], and pedestrian and traffic prediction [13][14][15]. However, modeling future image content and object motion is challenging due to dynamic evolution and image complexity, such as occlusions, camera movements, and illumination.…”

Section: Introductionmentioning

confidence: 99%

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Ranjan¹,

Bhandari²,

Kim³

et al. 2023

Computers, Materials &Amp; Continua

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Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiplebranch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

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Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

Cited by 17 publications

References 49 publications

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

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