Adaptive Traction Drive Control Algorithm for Electrical Energy Consumption Minimisation of Autonomous Unmanned Aerial Vehicle

Wai R., Prasetia A.S.

A surveillance system is one of the most interesting research topics for an unmanned aerial vehicle (UAV). However, the problem of planning an energy-efficient path for the surveillance purpose while anticipating disturbances and predicting energy consumptions during the path tracking is still a challenging problem in recent years. The optimal path planning and the disturbance rejection control for a UAV surveillance system are investigated in this paper. A trained and tested energy consumption regression model is used to be the cost function of an optimal path planning scheme, which is designed from a clustered 3D real pilot flight pattern with the proposed K-agglomerative clustering method, and is processed via A-star and set-based particle-swarm-optimization (S-PSO) algorithm with adaptive weights. Moreover, an online adaptive neural network (ANN) controller with varied learning rates is designed to ensure the control stability while having a reliably fast disturbance rejection response. The effectiveness of the proposed framework is verified by numerical simulations and experimental results. By applying the proposed optimal path planning scheme, the energy consumption of the optimal path is only 72.3397 Wh while the average consumed energy of real pilot flight data is 96.593Wh. In addition, the proposed ANN control improves average root-mean-square error (RMSE) of horizontal and vertical tracking performance by 49.083% and 37.50% in comparison with a proportional-integral-differential (PID) control and a fuzzy control under the occurrence of external disturbances. According to all of the results, the combination of the proposed optimal path planning scheme and ANN controller can achieve an energy-efficient UAV surveillance systems with fast disturbance rejection response.

Liu C., Feng W., Wang J., Chen Y., Ge N.

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme.

Ghazzai H., Ben Ghorbel M., Kassler A., Hossain M.J.

Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high- altitude flying relays to support communications between ground nodes due to their ability to provide line-of- sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the high-rate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.

Chen M., Dai F., Wang H., Lei L.

Flocking of the unmanned aerial vehicle (UAV) network refers to utilize the node’s autonomous mobility to satisfy the principle of cohesion, separation, and alignment. A network with flocking ensures the connectivity between high-speed UAV nodes and simplifies the design of various swarm applications. In this paper, we propose a novel distributed flocking model for UAV swarm networks. The model follows the Boid principle and establishes the master–slave transmission mode among the nodes. The slave node performs the distributed autonomous regulation. An effective flocking method is proposed, which is based on the positioning and communication capabilities of Wi-Fi. The slave node can sense and adjust their distance and direction from the master node. The simulation and experimental results show that our model can guarantee the connectivity between all nodes and has $1.4\times $ the network goodput gain outperforms the traditional flying ad hoc network.

Apse-Apsitis P., Avotins A., Porins R.

In this paper we report a power monitoring system used in the industrial greenhouse. Monitored data is processed in the device and then transferred to secure cloud services, where the data can be further processed, formatted and sent to the server used for data storage and later analysis. The power monitoring system is a part of the project where various parameters are measured in the greenhouse and as a result, could offer guidelines for the more efficient growth of plants [1] [2].

Spedicato S., Notarstefano G.

In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing reformulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterize the vehicle position and a spatial parameter is used as independent variable. This reformulation allows us to: (1) obtain a fixed horizon problem and (2) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nanoquadrotor in order to show its feasibility.

Strupka G., Rankis I.

Paper presents unmanned aerial vehicle (UAV) usage options and importance to fulfil power engineering tasks, improvements and their explanation. This paper also is part from research in this field restarted this year based also on previous research connected with power consumption problems [1] and propose initially data according topology without full coverage of data necessary to present final data.

Li L., Wu J., Xu Y., Che J., Liang J.

Unmanned Aerial Vehicles (UAVs) are widely used in civil and military fields. For a working UAV, the energy is limited, the coverage distance is short and the loading capacity is restricted. In order to execute more tasks in complex environment, there is a trend that UAVs are used in cluster and coordinated with each other. Wireless charging technology can charge the unmanned aerial vehicles in time and effectively extend the life of UAVs to reduce the number of disabled UAVs. In this paper, the scheduling algorithm for charging UAVs cluster is studied. By comparing with other optimization algorithms, the most suitable charge scheduling control algorithm can be evaluated according to the number of surviving nodes in a certain time in the rechargeable unmanned aircraft group.

Liu Y.D., Ziarek L.

Unmanned Aerial Vehicles (UAVs) are an emerging platform with promising applications in merchandise delivery, geographical surveillance, disaster management, to name a few. Energy consumption is a first-class concern in UAV design, as the majority of today's lightweight UAVs are either battery-powered or solar-powered. In this talk, we describe a first step toward developing sustainable tasks and applications, through a sustainability-aware programming level to the domain of UAV software development.

Bezzo N., Mohta K., Nowzari C., Lee I., Kumar V., Pappas G.

In this paper we consider an online planning problem for unmanned aerial vehicle (UAV) operations. Specifically, a UAV has the task of reaching a goal from a set of possible goals while minimizing the amount of energy required. Due to unforeseen disturbances, it is possible that initially attractive goals might end up being very expensive during the execution. Thus, two main problems are investigated here: i) how to predict and plan the motion of the UAV at run time to minimize its energy consumption and ii) when to schedule next replanning time to avoid unnecessary periodic re-evaluation executions. Our approach considers a nonlinear model of the system for which a model predictive controller is used to determine the desired control inputs for each possible goal. These control inputs are then used to estimate the energy required to reach the different goals. Finally, a self-triggered scheduling policy determines how long to wait before replanning the goal to aim for. The proposed framework is validated through simulations and experiments in which a quadrotor must choose and reach some goal while being subject to external disturbances.

Morbidi F., Cano R., Lara D.

A major limitation of existing battery-powered quadrotor UAVs is their reduced flight endurance. To address this issue, by leveraging the electrical model of a brushless DC motor, we explicitly determine minimum-energy paths between a predefined initial and final configuration of a quadrotor by solving an optimal control problem with respect to the angular accelerations of the four propellers. As a variation on this problem, if the total energy consumption between two boundary states is fixed, minimum-time and/or minimum-control-effort trajectories are computed for the aerial vehicle. The theory is illustrated for the DJI Phantom 2 quadrotor in three realistic scenarios.

Liu Y., Van Schijndel J., Longo S., Kerrigan E.C.

A nonlinear model predictive control strategy is presented for unmanned aerial vehicle (UAV) trajectory determination. The objective is to find optimal paths in the atmosphere by maximizing the UAV's energy (kinetic and potential) over a finite but receding horizon. The main assumption is that the updraft distribution is unknown, creating a realistic situation. The updrafts are only estimated online using standard on-board inertial sensors. Real-time implementation of the algorithm is shown to be possible in principle.

Mahony R., Kumar V., Corke P.

This article provides a tutorial introduction to modeling, estimation, and control formultirotor aerial vehicles that includes the common four-rotor or quadrotor case.

Economou J.T., Kladis G., Tsourdos A., White B.A.

Salazar F., Guamán-Molina J., Romero-Mediavilla J., Arias-Espinoza C., Zurita M., Jhonny C., Martinez-García S., Castro A.

A Flying Ad Hoc Network (FANET) is a new type of network derived from MANET, these networks use as nodes of unmanned aerial vehicles (UAV) that can be equipped with positioning, vision, or other systems. Currently, they present some advantages such as collaborative work between UAVs improving efficiency compared to single UAV systems. FANET’s capacity to cover large geographical areas has already positioned it as one of the best alternatives for traffic monitoring and control in smart cities. On the other hand, these networks present some challenges and problems that must be considered at the time of their design. The analysis of these networks using two-dimensional models, in some cases propose the use of a UAV in a stationary way in a specific area. Therefore, in this work we propose the implementation of a 3D model, allowing to generate a realistic environment to the movement of a UAV, using the Gauss Markov mobility model evaluating Ad Hoc routing protocols OLSR, AODV and DSDV. For the analysis of the results, the Network Simulator version 3 (Ns-3) software is used to simulate a FANET network, evaluating the efficiency of the routing protocols. The results obtained from the simulation and implementation of the proposed network showed that the OLSR protocol presents better efficiency maximizing the scope of traffic monitoring. Finally, the FANET network with 4 heterogenous drones, allowed to improve the coverage of a larger geographical area with an adequate performance of the protocol making it suitable for remote traffic monitoring in a smart city environment.

Jacewicz M., Żugaj M., Głębocki R., Bibik P.

In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).

Zinchenko S., Mateichuk V., Nosov P., Popovych I., Solovey O., Mamenko P., Grosheva O.

Abstract One of the ways to reduce human influence on the control process is the development of automated and automatic control systems. Modern control systems are quite complex and require preliminary ground testing. The article considers the issues of creating Imitation Modelling Stand for such control system synthesis and testing. For this reason, a Control System Model was integrated into the local computer network of the navigation simulator NTPRO 5000. The authors of the paper developed and tested software for information exchange between the navigation simulator and the Control System Model. The authors also developed a functional module of collision avoidance with many targets for testing in a closed loop system with virtual training objects. The results showed that the developed Imitation Modelling Stand allowed developing and testing functional modules of the control systems. In comparison with the found analogues, it is easy to include in a closed simulation cycle various models of command devices, actuators, control objects, objects of training scene, weather conditions; it is universal both for solving problems of manual control and for developing and testing automatic and automated control systems; it is not highly specialised and is created at minimal costs.