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 Design and Dynamics of Kinetic Launcher for Unmanned Aerial Vehicles

2020 ◽  
Vol 10 (8) ◽  
pp. 2949
Author(s):  
Mirosław Kondratiuk ◽  
Leszek Ambroziak
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Electrical Energy ◽  
Practical Implementation ◽  
The Novel ◽  
Electromagnetic Launchers ◽  
Aerial Vehicles ◽  
Start Up ◽  
Motion Characteristics ◽  
Novel Concept ◽  
Operator Safety

Assisting in the starting procedure of Unmanned Aerial Vehicles (UAVs) is one of many very important areas of modern aviation research. Supported start-up saves fuel or electrical energy, increases operator safety and level of autonomy, extends the application area, and, in some applications, even enables the operator to shape the motion characteristics of the initial phase of a UAV’s flight. Currently used solutions, depending on an aircraft’s class, are based on the utilization of rubber, pneumatic or electromagnetic launchers. All of these launchers are used for the medium class of UAVs and all of them use the potential energy previously stored in stretched rubber, compressed air or electrical voltage. In this paper, authors propose the novel concept of a launcher powered through kinetic energy stored in a rotary wheel driven by an electric motor. Using the transmission systems of the drive and the controlled clutch and an electromagnetic brake, it is possible to precisely control the speed and acceleration of the launched object. Within the paper, the authors present and discuss the applied equations of dynamics, the results of a simulation that was carried out using the MATLAB/Simulink software and a conceptual CAD model of preliminary engineering solutions for the kinetic UAV launcher. The work is summarized in the conclusions section, which details the practical implementation of the device.

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.

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.

ALGORITHM FOR PRECISE LANDING OF MULTIROTOR UNMANNED AIRCRAFT SYSTEMS AT AN AUTONOMOUS CHARGING STATION

2021 ◽  
Vol 4 (30) ◽  
pp. 3-10
Author(s):  
E. A. Voznesenskii ◽  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Block Diagram ◽  
Unmanned Aircraft ◽  
Night Vision ◽  
Unmanned Aircraft Systems ◽  
Charging Station ◽  
Aircraft Systems ◽  
Aerial Vehicles ◽  
Recognition Result ◽  
Position And Orientation

In this article, we propose an algorithm for accurately landing multirotor (quadcopters, hexacopters, etc.) unmanned aerial vehicles (UAVs) at an autonomous charging station. This article also presents methods for locating the charging station and landing the UAV at night. Section 1 describes the general sequential landing procedures. Section 2 describes methods for detecting the ArUco marker and evaluating its position and orientation using the OpenCV computer vision library and shows the recognition result. In section 3, the precise landing algorithm is analyzed in detail, and a block diagram of the algorithm is given. Section 4 discusses the integration of the night vision camera into the landing algorithm.

A Piezocomposite Actuated Propeller With an Onboard Power Generator

2020 ◽  
Author(s):  
Bharg Shah ◽  
Onur Bilgen
Keyword(s):  
Solid State ◽  
Unmanned Aerial Vehicles ◽  
Rotor Blade ◽  
Mechanical Energy ◽  
Electrical Energy ◽  
Power Generator ◽  
Single Degree ◽  
Model Scale ◽  
Aerial Vehicles ◽  
Electromagnetic Power

Abstract This paper presents an application of a novel piezocomposite rotor system on a model-scale helicopter. The piezocomposite rotor concept can be implemented on various rotary systems, including small unmanned aerial vehicles, and tandem rotor, multi-rotor, single-rotor and other rotary systems. Based on authors’ previous research, a new design of the so-called solid-state rotor concept is implemented on a single degree of the freedom apparatus that the derived from the model-scale helicopter. An electromagnetic power generator is used to convert the mechanical energy due to the rotation of the generator into electrical energy, and this is used to actuate the piezocomposite blades. The actuation by the piezocomposite actuators on the rotor blade results in a change of the camber of the rotor blade. The change in camber generates an increase of thrust.

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.

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