Energy-Aware Navigation and Guidance Algorithms for Unmanned Aerial Vehicles

2011 ◽  
Author(s):  
Ondrej Špinka ◽  
Zdenek Hanz´lek
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Energy Aware ◽  
Aerial Vehicles

Scheduling UAV’s on Autonomous Charging Station

2021 ◽  
Author(s):  
Rafał Różycki ◽  
Tomasz Lemański ◽  
Joanna Józefowska
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Electrical Energy ◽  
Optimization Criterion ◽  
Energy Aware ◽  
Constant Energy ◽  
Charging Station ◽  
Aerial Vehicles ◽  
Charging Current ◽  
Power Limit ◽  
Computing Module

The paper considers the concept of a charging station for an Unmanned Aerial Vehicles (UAV, drone) fleet. The special feature of the station is its autonomy understood as independence from a constant energy source and an external module for managing its operation. It is assumed that the station gives the possibility to charge batteries of many drones simultaneously. However, the maximum number of simultaneously charged drones is limited by a temporary total charging current (i.e. there is a power limit). The paper proposes a mathematical model of charging a single drone battery. The problem of finding a schedule of charging tasks is formulated, in which the minimum time of the charging process for all drones is assumed as the optimization criterion. Searching for a solution to this problem is performed by an autonomous charging station with an appropriate computing module equipped with a Variable Speed Processor (VSP). To that end an appropriate algorithm is activated (i.e. a computational job), the execution of which consumes a certain amount of limited energy available to the charging station. In the paper we consider energy-aware execution of an implementation of an evolutionary algorithm (EA) as a computational job. The possibility of saving energy by controlling the CPU frequency of a VSP is analyzed. A characteristic feature of the processor is the non-linear relationship between the processing rate and electric power usage. According to this relationship, it turns out that slower execution of the computational job saves electrical energy consumed by the processor.

scholarly journals Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Saqib Majeed ◽  
Adnan Sohail ◽  
Kashif Naseer Qureshi ◽  
Arvind Kumar ◽  
Saleem Iqbal ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Cellular Networks ◽  
Residual Energy ◽  
Area Network ◽  
Wide Area Network ◽  
Test Bed ◽  
Energy Aware ◽  
Available Energy ◽  
Aerial Vehicles ◽  
Minimum Number

AbstractCellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

scholarly journals Energy-aware Coverage Path Planning for Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo Ferreira Jr.
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Partial Information ◽  
State Of The Art ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Aerial Vehicles ◽  
Novel Approaches ◽  
Grid Based

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware pheromone-based solution for patrolling missions called NC-Drone. The three novel approaches successfully address different coverage path planning scenarios, advancing the state-of-the-art in this area.

scholarly journals A Design Methodology for Energy-Aware Processing in Unmanned Aerial Vehicles

2022 ◽  
Vol 27 (1) ◽  
pp. 1-20
Author(s):  
Jingyu He ◽  
Yao Xiao ◽  
Corina Bogdan ◽  
Shahin Nazarian ◽  
Paul Bogdan
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Homeland Security ◽  
Design Methodology ◽  
Energy Savings ◽  
Visual Navigation ◽  
Parallel Execution ◽  
Disaster Mitigation ◽  
Energy Aware ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have rapidly become popular for monitoring, delivery, and actuation in many application domains such as environmental management, disaster mitigation, homeland security, energy, transportation, and manufacturing. However, the UAV perception and navigation intelligence (PNI) designs are still in their infancy and demand fundamental performance and energy optimizations to be eligible for mass adoption. In this article, we present a generalizable three-stage optimization framework for PNI systems that (i) abstracts the high-level programs representing the perception, mining, processing, and decision making of UAVs into complex weighted networks tracking the interdependencies between universal low-level intermediate representations; (ii) exploits a differential geometry approach to schedule and map the discovered PNI tasks onto an underlying manycore architecture. To mine the complexity of optimal parallelization of perception and decision modules in UAVs, this proposed design methodology relies on an Ollivier-Ricci curvature-based load-balancing strategy that detects the parallel communities of the PNI applications for maximum parallel execution, while minimizing the inter-core communication; and (iii) relies on an energy-aware mapping scheme to minimize the energy dissipation when assigning the communities onto tile-based networks-on-chip. We validate this approach based on various drone PNI designs including flight controller, path planning, and visual navigation. The experimental results confirm that the proposed framework achieves 23% flight time reduction and up to 34% energy savings for the flight controller application. In addition, the optimization on a 16-core platform improves the on-time visit rate of the path planning algorithm by 14% while reducing 81% of run time for ConvNet visual navigation.

Energy Aware Enhanced Krill Herd Algorithm Enabled Clustering for Unmanned Aerial Vehicles

2021 ◽  
pp. 17-25
Author(s):  
Mohamed Elhoseny ◽  
◽  
X. Yuan ◽  
Mohamed Abdel-basset ◽  
◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
High Mobility ◽  
Search Space ◽  
Energy Utilization ◽  
Energy Aware ◽  
Krill Herd Algorithm ◽  
Aerial Vehicles ◽  
Krill Herd ◽  
Energy Efficient Clustering

Recently, unmanned aerial vehicles (UAV) have gained maximum interest in diverse applications ranging from military to civilian areas. The presence of numerous energy-constrained UAVs in an adhoc manner poses several design issues. At the same time, the limited battery, high mobility, and adaptive characteristics of the UAVs need effective design of clustering techniques for UAVs. In this manner, this paper presents a levy flight with a krill herd optimization algorithm (LF-KHOA) for energy-efficient clustering in UAVs. The proposed LF-KHOA technique integrates the concepts of LF to the KHOA to enhance efficiency and search space exploration. In addition, the LF-KHOA technique derives a fitness function involving three input parameters to elect cluster heads (CHs) and organize clusters. The energy consumed by the UAVs depends on the distance from UAVs to nearby nodes. Therefore, the fitness function aims to decrease communication distance, which mitigates energy utilization when transmitting the information. To ensure the better performance of the LF-KHOA technique, an extensive set of simulations takes place, and the results are inspected in terms of different measures. The experimental results highlighted the betterment of the LF-KHOA technique over the current state of art techniques.

Development of thickened semi-synthetic engine oil M-5z / 20 AERO for four-stroke gasoline engines aircraft piston of unmanned aerial vehicles (UAVs)

2020 ◽  
Vol 06 ◽  
pp. 44-47
Author(s):  
A.A. Moykin ◽  
◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Engine Oil ◽  
Motor Oil ◽  
Gasoline Engines ◽  
State Research Institute ◽  
Technical Requirements ◽  
Aerial Vehicles ◽  
State Research ◽  
Industrial Batch ◽  
The Russian Federation

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

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