Autonomous Control and Transportation of Underslung Load with Single and Dual Lift Helicopter Systems

This paper discusses the development of a single lift and dual-lift helicopter underslung load transportation system for practical applications. A control law is developed to damp the load swing and stabilize the oscillation while performing the transportation task. For the dual-lift system, the load transportation is achieved by using a load distribution controller, developed for this purpose, to maintain equal load distribution among the vehicles. The load damping and load distribution controllers require accurate measurement of load states, which is achieved through the design and development of innovative, simple, and lightweight sensors units namely, Load Tension Measurement Unit (LTMU) and Load Swing Measurement Unit (LSMU). LTMU sensor consists of a unique design that utilizes a flexi-force sensor, capable of measuring compressive load, for measurement of cable tension. The cable inclination in the longitudinal and lateral directions is measured by the LSMU sensor. These units are integrated with the helicopter autopilot for autonomous flight. The performance of the developed system is experimentally validated in the outdoor environment with single and dual-lift systems.

Flight Planning Optimization of Multiple UAVs for Internet of Things

This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.

Estimating the oscillation frequency of a payload mass on quadcopter in non-autonomous flight

Unmanned aerial vehicles (UAVs) have been employed in several engineering applications, such as aerial photography, environmental surveillance, delivery tasks, and others. Most of these applications have attracted increasing attention due to their ability to carry different payloads, which can change the dynamic of flight. The present article investigates the dynamics of a quadcopter with a payload mass including the stiffness of the attachment to the aerial vehicle. An approach to obtain non-dimensional equations of motion is introduced, and as a consequence, it is possible to determine the frequency of vibration of the payload mass during the flight. The results are presented for non-autonomous flight, and they establish a simple equation to estimate the oscillation frequency depending on the relation between the quadcopter and the payload masses, also considering the stiffness of attachment, without requiring a solution to the equation of motion in the time domain.

Prototype Development of Cross-Shaped Microphone Array System for Drone Localization Based on Delay-and-Sum Beamforming in GNSS-Denied Areas

Drones equipped with a global navigation satellite system (GNSS) receiver for absolute localization provide high-precision autonomous flight and hovering. However, the GNSS signal reception sensitivity is considerably lower in areas such as those between high-rise buildings, under bridges, and in tunnels. This paper presents a drone localization method based on acoustic information using a microphone array in GNSS-denied areas. Our originally developed microphone array system comprised 32 microphones installed in a cross-shaped configuration. Using drones of two different sizes and weights, we obtained an original acoustic outdoor benchmark dataset at 24 points. The experimentally obtained results revealed that the localization error values were lower for 0∘ and ±45∘ than for ±90∘. Moreover, we demonstrated the relative accuracy for acceptable ranges of tolerance for the obtained localization error values.

Smart Search System of Autonomous Flight UAVs for Disaster Rescue

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment

The ability of an autonomous Unmanned Aerial Vehicle (UAV) in an unknown environment is a prerequisite for its execution of complex tasks and is the main research direction in related fields. The autonomous navigation of UAVs in unknown environments requires solving the problem of autonomous exploration of the surrounding environment and path planning, which determines whether the drones can complete mission-based flights safely and efficiently. Existing UAV autonomous flight systems hardly perform well in terms of efficient exploration and flight trajectory quality. This paper establishes an integrated solution for autonomous exploration and path planning. In terms of autonomous exploration, frontier-based and sampling-based exploration strategies are integrated to achieve fast and effective exploration performance. In the study of path planning in complex environments, an advanced Rapidly Exploring Random Tree (RRT) algorithm combining the adaptive weights and dynamic step size is proposed, which effectively solves the problem of balancing flight time and trajectory quality. Then, this paper uses the Hermite difference polynomial to optimization the trajectory generated by the RRT algorithm. We named proposed UAV autonomous flight system as Frontier and Sampling-based Exploration and Advanced RRT Planner system (FSEPlanner). Simulation performs in both apartment and maze environment, and results show that the proposed FSEPlanner algorithm achieves greatly improved time consumption and path distances, and the smoothed path is more in line with the actual flight needs of a UAV.

ANALYSIS OF TRAFFIC CONGESTION AT TRANSPORT TRANSFER HUBS USING DRONES

This article considers the possibility of using drones to identify and analyze the congestion of traffic-transfer hubs in the urban environment. The article substantiates the need to analyze the congestion of traffic-transfer hubs, due to the unevenness of traffic congestion. Classical models of congestion estimation do not allow taking into account the dynamics of the process. The data obtained by manual calculations contain errors and require lengthy processing. Using the data from cameras allows solving the problem of congestion analysis, but it is not possible to solve this problem with high accuracy during peak hours. The use of data obtained from drones (from an unmanned aerial system) is proposed to solve the congestion analysis problem. To implement this solution, the article presented a list of necessary components that a drone should have, an example of the source code for the implementation of an autonomous flight, an example of the implementation of the traffic-transfer hubs overflight scheme using special software.