An efficient UAV navigation solution for confined but partially known indoor environments

Wang, Fei; Wang, Kangli; Lai, Shupeng; Phang, Swee King; Chen, Ben M.; Lee, Tong Heng

doi:10.1109/icca.2014.6871120

Cited by 28 publications

(22 citation statements)

References 8 publications

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“…This model without the consideration for the losses due to the module's internal series resistance, as well as contacts and interconnections between cells and modules in this dissertation. It has a relatively good approximation precision and it is perhaps the most suitable model for the Mobile robot navigation communication in industrial IoT environment, as it offers good compromise between approximation precision and simplicity [20]. From the current point P n , there has a unit vector pointing ℊ pointing to the destination points T, and let r k h be the vector from the point P n to the closest point at the circle B.…”

Section: T a B L E 1 Parameters Of The Mobile Robot Path Planning Mementioning

confidence: 99%

Mobile robot navigation path algorithm in 3d industrial internet of thing (iot) environment based on 5g mobile communication

Ping

Petrenko

2019

Sist. anal.i prikl. inform.

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With the significant growth of the semiconductor industry, creating small devices with powerful processing ability and network capabilities are no longer a dream for engineers. Currently, Internet of Things (IoT) has become one of the hottest topics in both industry and academia of wireless communication field. The idea of the Internet of Things (IoT) is to connect every day physical objects such as microwave, doors, lightings and so on. The technical concept of the IoT is to enables these different physical objects to sense information using sensors and sends this information to a server. Industrial IoT is to integrate various technologies to improve business services in different sectors. It implicitly indicates the behavior of machine-to-machine communications. Each of industrial IoT as service domains has its own communication requirements that are measured differently in both such as reliability, Quality of service (QoS), and privacy. According to the development of industrial automation, the industrial Internet of things (IoT) is widely used in smart factories to capture the data and manage the production. One of the most important components of industrial IoT is the wireless sensor network (WSN), which are easy to deploy and use in indoor industrial environments for a IoT of tasks, such as irrigation, machine condition monitoring, and environment monitoring. There are a IoT of works focusing on the WSN in industrial IoT. For example, in, a WSN-based hidden Markov model is proposed to estimate the occupancy in the building (also see other works [1], [2]). In this paper, the research focus on 3-D mobile robot tracking in 5G wireless communication combine to sensor network and mobile robot path planning. The mobile robot can move fast in the three-dimensional (3-D) indoor industrial environment for many tasks such as the monitoring of possible damages on industrial plants [3], data gathering and transmission from wireless communication [4], transportation [5].

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Section: T a B L E 1 Parameters Of The Mobile Robot Path Planning Mementioning

confidence: 99%

Mobile robot navigation path algorithm in 3d industrial internet of thing (iot) environment based on 5g mobile communication

Ping

Petrenko

2019

Sist. anal.i prikl. inform.

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“…Wang et al [9] present an autonomous UAV navigation solution for partially known indoors environments, using two 2D laser sensors and an IMU. For the estimation of the altitude, authors use the a 2D laser sensor mounted orthogonally with respect to the UAV.…”

Section: Sensor Fusion Based Techniquesmentioning

confidence: 99%

“…Kumar et al [10] present a UAV system with integration of two 2D laser sensors and IMU in industrial environments for real-time pipeline classification. The authors use similar approach for flight altitude estimation presented by [9], thus suffering from the similar limitations.…”

Section: Sensor Fusion Based Techniquesmentioning

confidence: 99%

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

et al. 2018

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This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

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“…Due to the high criterion for reliability and robustness of measurement system in industrial field, the network structure of common wireless sensor networks should be refined to fit in IIoT [18][19][20]. Too many tiers in IIoT will increase complexity of protocol management and hardware design, yet too few ters constrict network's flexibility and application areas.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

et al. 2018

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In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Qlearning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

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An efficient UAV navigation solution for confined but partially known indoor environments

Cited by 28 publications

References 8 publications

Mobile robot navigation path algorithm in 3d industrial internet of thing (iot) environment based on 5g mobile communication

Mobile robot navigation path algorithm in 3d industrial internet of thing (iot) environment based on 5g mobile communication

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

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