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Purpose This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV). Design/methodology/approach The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS). Findings All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system. Practical implications The proposed controllers are easily implementable on-board and are computationally efficient. Originality/value The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.

Abstract-A quadrotors is a type of Unmanned Aerial Vehicles (UAV) systems that attract the researchers in the control field since it's a highly nonlinear, underactuated system. In this paper, a non-linear dynamic model based on quaternions is developed. Differential flatness is an approach that enables the optimization to occur within the output space and therefore simplifies the problem of the trajectory tracking. The aim of this work is to create a Differential flatness-quaternion approach that enables the quadrotors to follow a desired path. The trajectory tracking is assured by a double loop control structure based on the LQR controller.

In order to deal with critical missions a growing interest has been shown to the UAVs design. Flying robots are now used fire protection, surveillance and search & rescue (SAR) operations. In this paper, a fully autonomous system for SAR operations using quadrotor UAV is designed. In order to scan the damaged area, speeds up the searching process and detect any possible survivals a new search strategy that combines the standard search strategies with the probability of detection is developed. Furthermore the autopilot is designed using an optimal backstepping controller and this enables the tracking of the reference path with high accuracy and maximizes the flying time. Finally a comparison between the applied strategies is made using a study case of survivals search operation. The obtained results confirmed the efficiency of the designed system.

In recent years, human–drone interaction has received increasing interest from the scientific community. When interacting with a drone, humans assume a variety of roles, the nature of which are determined by the drone’s application and degree of autonomy. Common methods of controlling drone movements include by RF remote control and ground control station. These devices are often difficult to manipulate and may even require some training. An alternative is to use innovative methods called natural user interfaces that allow users to interact with drones in an intuitive manner using speech. However, using only one language of interacting may limit the number of users, especially if different languages are spoken in the same region. Moreover, environmental and propellers noise make speech recognition a complicated task. The goal of this work is to use a multilingual speech recognition system that includes English, Arabic, and Amazigh to control the movement of drones. The reason for selecting these languages is that they are widely spoken in many regions, particularly in the Middle East and North Africa (MENA) zone. To achieve this goal, a two-stage approach is proposed. During the first stage, a deep learning based model for multilingual speech recognition is designed. Then, the developed model is deployed in real settings using a quadrotor UAV. The network was trained using 38,850 records including commands and unknown words mixed with noise to improve robustness. An average class accuracy of more than 93% has been achieved. After that, experiments were conducted involving 16 participants giving voice commands in order to test the efficiency of the designed system. The achieved accuracy is about 93.76% for English recognition and 88.55%, 82.31% for Arabic and Amazigh, respectively. Finally, hardware implementation of the designed system on a quadrotor UAV was made. Real time tests have shown that the approach is very promising as an alternative form of human–drone interaction while offering the benefit of control simplicity.

Nowadays, most of the recent researches are focusing on the use of multi-UAVs in both civil and military applications. Multiple robots can offer many advantages compared to a single one such as reliability, time decreasing and various simultaneous interventions. However, solving the formation control and obstacles avoidance problems is still a big challenge. This paper proposes a distributed strategy for UAVs formation control and obstacles avoidance using a consensus-based switching topology. This novel approach allows UAVs to keep the desired topology and switch it in the event of avoiding obstacles. A double loop control structure is designed using a backstepping controller for tracking of the reference path, while a Sliding Mode Controller (SMC) is adopted for formation control. Furthermore, collaborative obstacles avoidance is assured by switching the swarm topology. Numerical simulations show the efficiency of the proposed strategy.

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis , a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness.