Energy-Aware Coverage Path Planning of UAVs

Franco, Carmelo Di; Buttazzo, Giuseppe

doi:10.1109/icarsc.2015.17

Cited by 292 publications

(138 citation statements)

References 11 publications

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“…The energy consumption of each of these segments is also predetermined, and added up to yield E move . In this light, the total energy consumption for a path consisting of n sl straight-line segments, and n t turns can be calculated by [62] …”

Section: Energy-aware Motion Planningmentioning

confidence: 99%

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Energetics in robotic flight at small scales

Karydis

Kumar

2017

Interface Focus.

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Recent advances in design, sensing and control have led to aerial robots that offer great promise in a range of real-world applications. However, one critical open question centres on how to improve the energetic efficiency of aerial robots so that they can be useful in practical situations. This review paper provides a survey on small-scale aerial robots (i.e. less than 1 m 2 area foot print, and less than 3 kg weight) from the point of view of energetics. The paper discusses methods to improve the efficiency of aerial vehicles, and reports on recent findings by the authors and other groups on modelling the impact of aerodynamics for the purpose of building energy-aware motion planners and controllers.

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Section: Energy-aware Motion Planningmentioning

confidence: 99%

“…A different approach focuses on coverage path planning with energy-based optimization criteria [62]. Each path is designed as a concatenation of elementary manoeuvres, with predetermined energy consumption.…”

Section: Energy-aware Motion Planningmentioning

confidence: 99%

Energetics in robotic flight at small scales

Karydis

Kumar

2017

Interface Focus.

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“…Moreover, the Mechanical Energy Efficiency (MEE) (in bits/joule) can be defined as the ratio of the amount of total transmitted bits over the total mechanical energy consumption of the drone during the operation time. MEE can be computed by Drone Moving Power at h=10 m v=10 m/s 110 watt [12] M EE = (…”

Section: Cee = (mentioning

confidence: 99%

Dynamic Base Station Repositioning to Improve Performance of Drone Small Cells

Fotouhi

Ding

Hassan

2016

2016 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Thanks to the recent advancements in drone technology, it has become viable and cost-effective to quickly deploy small cells in areas of urgent needs by using a drone as a cellular base station. In this paper, we explore the benefit of dynamically repositioning the drone base station in the air to reduce the distance between the BS and the mobile user equipment, thereby improving the spectral efficiency of the small cell. In particular, we propose algorithms to autonomously control the repositioning of the drone in response to users activities and movements. We demonstrate that, compared to a drone hovering at a fixed location, dynamic repositioning of the drone moving with high speed can increase spectral efficiency by 15%. However, considering the tradeoff between spectral efficiency and energy efficiency of the drone, we show that 10.5% spectral efficiency gain can be obtained without negatively affecting energy consumption of the drone.

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“…This creates difficulties when controlling the stability of the flight in windy circumstances, very low and high flight altitude as well as when making sharp turns. These difficulties are major issues to be dealt with in optimization tasks such as determination of shortest path and trajectory planning [1,[7][8][9], coverage based path planning [2,10], or obstacle avoidance [3] Stability problems are often discussed from cybernetic point of view i.e. : control systems, optimization [13,14], control algorithms [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent passively stabilized quadrotor

Sayfeddine

Bulgakov

Kruglova

2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Quadrotor stability is one of the most topical researches worldwide. It is due to the simplicity, availability and cost of such platform. This miniature aerial vehicle is highly manoeuvrable, straight forward to use and to maintain. It can be deployed to perform wide variety of tasks. On the other hand, the quadrotor suffers from non-stability, which makes it unreliable, especially when flying on low speed, high altitude and in windy circumstances. This paper discusses the improvement of the quadrotor by adding a stabilizing mechanism working as a passive breaking system in sharp and spontaneous turns. The mechanism is described and simulated as a standalone module. The end result represents the determination of the stiffness coefficient of the stabilizing actuator using fuzzy logic controller. IntroductionResearching and optimizing Unmanned Aerial Vehicles (UAVs) is very topical. It is due to the developed commercial potential of this market, which lead to their exploitation for civil tasks. Traditionally UAV are used in the military field. However, nowadays, UAVs are being deployed into cinematographic, rescue, exploration and delivery tasks. UAVs are classified according to their type, size and tasks designed for. A rotorcraft type such as quadrotor is affordable products, easy to operate and maintain and does not require supporting infrastructures. As a vertical take-off and landing UAV, the quadrotor can start from any place and land without any difficulties. The maneuverability of the quadrotor is a major benefit. This simple UAV is used for landscaping, scanning high-rise buildings and monitoring. On the other hand, the low flight range, fragile chassis in comparison to other UAVs, and its non-stability; especially when flying at high altitude or in windy circumstances minimize the tasks variety, where it can be deployed.Many researches concentrate on optimizing the control systems, construction materials, path planning algorithms and generation of adequate mathematical models of the quadrotor. Our contribution to this topic is not far from the worldwide tendency. This paper describes passive stabilization mechanism for the quadrotor. The mechanical components to realize the idea and the control scheme are disscissed in paragraph 3, simulations and results in paragraph 4. While the bibliographic review on the subject matter is addressed in paragraph 2.

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Energy-Aware Coverage Path Planning of UAVs

Cited by 292 publications

References 11 publications

Energetics in robotic flight at small scales

Energetics in robotic flight at small scales

Dynamic Base Station Repositioning to Improve Performance of Drone Small Cells

Intelligent passively stabilized quadrotor

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