UAV obstacle avoidance using potential field under dynamic environment

SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

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“…For dynamic routing planning, potential field method (PFM) is used 70 . The efficient path is chosen based on total fields from start to end.…”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

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“…Furthermore, we assume that a UAV is installed with a storage device sufficiently large to load the data for the terrestrial nodes in the RoI from the emergency operations center. Although UAV control-related issues such as obstacle, collision avoidance, or skewed movement are important factors and investigated [33]- [35], they are not mainly focused on, and out of scope in this paper.…”

Section: System Overviewmentioning

confidence: 99%

Time-Dependent Ad-Hoc Routing Structure for Delivering Delay-Sensitive Data Using UAVs

et al. 2020

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In disaster scenarios where communication networks have broken down, it is important to ensure a reliable data delivery from an emergency operations center to the local target nodes within its effective time limit. We propose a hybrid data delivery mechanism that exploits the load-carry-and-delivery by UAVs with a mixture of localized ad-hoc routing over partially connected terrestrial networks. We aim to achieve reliable on-time data delivery to the target nodes, while preserving the low routing cost. Our proposed routing methodology consists of three steps: 1) localized network construction, 2) network probing by UAVs, and 3) localized ad-hoc routing based on a dynamic depth routing tree depending on the data urgency. Here, we present an innovative cost-effective local data sharing structure called localized minimal routing tree that balances with the direct data delivery by UAVs. After the initial network setup and probing procedure, each UAV makes a series of near-optimal decisions of which grid points to visit considering its localized network topology and data urgency. Our time-dependent routing mechanism dynamically decides data recipient nodes to serve more urgent data delivery with a higher priority at a time. The simulation experiments validated our combined path planning and routing approach, achieving 71% higher reliability than the best possible performance by using only the network nodes and consuming 20% lower energy than the UAV-only approach, while maintaining high reliability. Thus, our work makes a strong case for systematically combining the two approaches. INDEX TERMS Time-dependent routing, delay constraints, load-carry-and-delivery, vehicular ad-hoc networks, unmanned aerial vehicles, ad-hoc data delivery.

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“…This can be observed in Schlenzig et al (1994), Cuntoor and Chellappa (2007), and Kulić et al (2012). However, those approaches are concerned mainly with action identification of a single subject (or a single actor), such as in Yilmaz and Shah (2005) and Budiyanto et al (2015). Usually, they require high-quality images or other sensors to be attached to the human body.…”

Section: Introductionmentioning

confidence: 99%

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

Pinto

Melo

Marcato

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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There is an intensive growth in researches regarding surveillance and threat detection. Surveillance tasks often involve several actors with multiple interactions. Thus, modeling a complex activity becomes challenging. This work proposes an architecture comprised of low, middle, and high levels. The low-level recognizes characteristics, positioning of objects, and time of occurrences utilizing a camera and Unmanned Aerial Vehicle (UAV) sensors. The middle-level is responsible for structuring the information from the low-level using Deterministic Finite Automata (DFA). An expert system attached in the high-level module performs inference over the organized information to enables the system to have simple reasoning modules, assisting the operator decision. The architecture is embedded in a UAV to reduce the number of cameras and to reach difficult areas. The experiments showed that the proposed system updated the grammatical structure effectively given a sequence of information computed by the vision modules.

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UAV obstacle avoidance using potential field under dynamic environment

Cited by 62 publications

References 14 publications

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Time-Dependent Ad-Hoc Routing Structure for Delivering Delay-Sensitive Data Using UAVs

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

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