Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles is the task allocation architecture. This approach allows tasks to be efficiently assigned to robots of a multi-agent system, taking into account both the robots’ capabilities and service requirements. Consequently, this study provides an overview of the application of drones in ITSs, focusing on the applications of task allocation algorithms for UAV networks. Currently, there are different types of algorithms that are employed for task allocation in drone-based intelligent transportation systems, including market-based approaches, game-theory-based algorithms, optimization-based algorithms, machine learning techniques, and other hybrid methodologies. This paper offers a comprehensive literature review of how such approaches are being utilized to optimize the allocation of tasks in UAV-based ITSs. The main characteristics, constraints, and limitations are detailed to highlight their advantages, current achievements, and applicability to different types of UAV-based ITSs. Current research trends in this field as well as gaps in the literature are also thoughtfully discussed.

Unmanned aerial vehicles (UAVs) have been used to transport a variety of payloads, including medical materials, foods, and different electronic devices. This type of innovation generates important concerns regarding payload integrity, mainly when involving medical goods that can be affected by vibration. This paper presents the flight dynamics of suspended payload carried by an UAV, considering a flexible attachment between a quadrotor and its payload. Adaptive sliding mode control (ASMC) is proposed to enhance the trajectory tracking and consequently minimize the payload vibration. Adaptive coefficients are introduced to compensate the payload dynamics and uncertainties, which are computed for each proposed trajectory and cargo mass. The proposed adaptive control can efficiently compensate the extra dynamics and uncertainties caused by the attached payload, regardless of the chosen trajectory and weight of the cargo. A root mean square–based index is used to quantitatively evaluate the oscillations. The results show a significant attenuation of the undesired oscillations up to 27%, when the adaptive control is used. Therefore, the use of the proposed ASMC aims to guarantee an improved trajectory tracking and the attenuation of residual oscillations on the main state variables, allowing to be safely employed for transporting several types of medicines, including those sensitive to vibration and those that are dropped during a flight.

Unmanned Aerial Vehicles (UAVs) have been used extensively in many applications, such as surveillance, medical transportation and delivery tasks. These applications usually involve an attachment system to connect a payload. However, when connected to such a coupled system, the UAV exhibits lower flight performance mainly due to gust loads. Mathematical modeling is used in this paper to illustrate the comparison between an UAV with a coupled system and a conventional UAV. Proportional Derivative (PD) and Sliding Mode Control (SMC) approaches are used to evaluate the vibrations of the payload, and the UAV trajectory subjected to Dryden gust. This work therefore uses these methods to investigate the dynamic response of a quadrotor-type UAV equipped with a payload and a flexible attachment system.

Unmanned aerial vehicles (UAVs) have been gaining increased importance due to their variety of applications. In some specific tasks, UAVs require the addition of payloads and onboard components, including sensors, which require great stability to provide safe and reliable responses (related to the payload characteristics, such as the temperature, pressure, vibrations and many other factors). In contrast with the suspended payloads carried by a quadrotor aircraft with a rigid attachment, an elastic attachment is designed to assess the influence of the vibration characteristics on the quadrotor and its payload. Since the payload dynamics can alter the flight performance, sensor measurement accuracy and payload integrity, an adapted sliding mode control is used to guide the quadrotor on its desired trajectory and to compensate for the payload dynamics. To reduce the need for position sensors, a reduced-dimension observer is designed to estimate the payload trajectory, as well as the external disturbance behavior. Numerical simulations are performed to demonstrate that the flexibility influences the quadrotor’s dynamics and can create residual oscillation on its payload.

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.