Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle

Ivanovas, Adomas; Ostreika, Armantas; Maskeliūnas, Rytis; Damaševičius, Robertas; Połap, Dawid; Woźniak, Marcin

doi:10.1007/978-3-319-91253-0_6

Cited by 13 publications

(15 citation statements)

References 23 publications

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“…Ivanovas et al [12] proposed an obstacle detection and avoidance approach for a UAV. Their approach uses computer vision techniques for detecting static obstacles in stereo camera images.…”

Section: Results and Analysismentioning

confidence: 99%

“…A quadrotor uses two pairs of identical, vertically oriented propellers of which one pair spins clockwise and the other spins counterclockwise. Commercially-available quadrotors are increasingly been used in a variety of applications such as monitoring and surveillance, search and rescue operations, geographic mapping, photography and filming, wildlife research and management, media coverage of public events, remote sensing for agricultural applications, and aerial package delivery [2,9,12,21]. Efficient and scalable solutions for these applications require an online path generation and navigation system for multiple UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire swarm. In contrast to the existing works [3,[6][7][8][9][11][12][13][14][15]18,19,24], our proposed system does not depend on a planning phase and produces efficient, collision-free paths in an online manner. We focus on collision prediction and avoidance and online path generation and navigation for swarms of UAVs.…”

Section: Introductionmentioning

confidence: 99%

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Online Path Generation and Navigation for Swarms of UAVs

Ashraf

Majd

Troubitsynå

2020

Scientific Programming

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With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-tomoving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.

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“…Ivanovas et al [12] proposed an obstacle detection and avoidance approach for a UAV. Their approach uses computer vision techniques for detecting static obstacles in stereo camera images.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Path Generation and Navigation for Swarms of UAVs

Ashraf

Majd

Troubitsynå

2020

Scientific Programming

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“…The achieved result demonstrates that our method could already be used for several applications such as infrared security cameras. Based on the main concepts used in the development of the method presented in this paper, we plan to work further on the development of new methods for infrared object tracking in images with dynamic backgrounds and cluttered object space, focusing on such applications as autonomous driving or military applications (such as those described in [57,58]) based on the concepts and ideas which were successfully validated in this paper.…”

Section: Discussion and Final Remarksmentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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“…Since the areas where drones are used are expanding with new applications proposed every year, it is important that drones continue to be used both safely and efficiently in multitude of different environments, while ensuring the avoidance of obstacles [50]. For example, the meteorology drones collect data on air temperature, humidity, pressure, wind force, radiation, etc.…”

Section: Discussionmentioning

confidence: 99%

Design of Fully Automatic Drone Parachute System with Temperature Compensation Mechanism for Civilian and Military Applications

Almadani

Svirskis

Narvydas

et al. 2018

Journal of Advanced Transportation

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Application of Unmanned Aerial Vehicles (a.k.a. drones) is becoming more popular and their safety is becoming a serious concern. Due to high cost of top-end drones and requirements for secure landing, development of reliable drone recovery systems is a hot topic now. In this paper, we describe the development of a parachute system with fall detection based on accelerometer-gyroscope MPU – 6050 and fall detection algorithm based on the Kalman filter to reduce acceleration errors while drone is flying. We developed the compensation algorithm for temperature-related accelerometer errors. The parachute system tests were performed from a small height on a soft surface. Later, the system was tested under real-world conditions. The system functioned effectively, resulting in parachute activation times of less than 0.5s. We also discuss the civilian and military applications of the developed recovery system in harsh (high temperature) environment.

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Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle

Cited by 13 publications

References 23 publications

Online Path Generation and Navigation for Swarms of UAVs

Online Path Generation and Navigation for Swarms of UAVs

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Design of Fully Automatic Drone Parachute System with Temperature Compensation Mechanism for Civilian and Military Applications

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