This paper presents a novel quaternion-based nonsingular control system for underactuated vertical-take-off and landing (VTOL) Unmanned Aerial Vehicles (UAVs). Position and attitude tracking is challenging regarding singularity and accuracy. Quaternion-based Adaptive Backstepping Control (QABC) is developed to tackle the underactuated issues of UAV control systems in a cascaded way. Leveraging the virtual control (auxiliary control) developed in the QABC, desired attitude components and required thrust are produced. Afterwards, we propose Quaternion-based Sliding Mode Control (QASMC) to enhance the stability and mitigate chattering issues. The sliding surface is modified to avoid singularity compared to conventional SMC. To improve the robustness of controllers, the control parameters are updated using adaptation laws. Furthermore, the asymptotic stability of translational and rotational dynamics is guaranteed by utilizing Lyapunov stability and Barbalet Lemma. Finally, the comprehensive comparison results are provided to verify the effectiveness of the proposed controllers in the presence of unknown time-varying parameter uncertainties and significant initial errors. Keywords: Non-singular Sliding Mode Control, Adaptive Backstepping Control, Unit-quaternion, Drones, Unmanned Aerial Vehicles, Asymptotic Stability, Position and Orientation Control

This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km/h and acceleration close to 20 m/s^2, while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight.

This paper discusses developments for a multi-limb morphogenetic UAV, MorphoGear, that is capable of both aerial flight and ground locomotion. A hybrid path planning algorithm based on A* strategy has been developed enabling seamless transition between air-to-ground navigation modes, thereby enhancing robot's mobility in complex environments. Moreover, precise path following is achieved during ground locomotion with a Model Predictive Control (MPC) architecture for its novel walking behaviour. Experimental validation was conducted in the Unity simulation environment utilizing Python scripts to compute control values. The algorithms' performance is validated by the Root Mean Squared Error (RMSE) of 0.91 cm and a maximum error of 1.85 cm, as demonstrated by the results. These developments highlight the adaptability of MorphoGear in navigation through cluttered environments, establishing it as a usable tool in autonomous exploration, both aerial and ground-based.

In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio signatures. Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

We present a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be utilized to assess the impact of FDI attacks, benchmark attack detector performance, and validate the effectiveness of mitigation/reconfiguration strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework leverages high-fidelity simulations of Gazebo and a Motion Capture system to emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor measurements in real-time. We propose an empirical approach to faithfully recreate signal characteristics such as latency and noise in these measurements. Finally, we illustrate the efficacy of our proposed framework through a Mixed Reality experiment consisting of an emulated GNSS attack on an actual UAV, which (i) demonstrates the impact of false data injection attacks on GNSS measurements and (ii) validates a mitigation strategy utilizing a distributed camera network developed in our previous work. Our open-source implementation is available at \href{https://github.com/CogniPilot/mixed\_sense}{\texttt{https://github.com/CogniPilot/mixed\_sense}}

This paper introduces the GazeRace method for drone navigation, employing a computer vision interface facilitated by eye-tracking technology. This interface is designed to be compatible with a single camera and uses a convolutional neural network to convert eye movements into control commands for the drone. Experimental validation demonstrates that users equipped with the eye-tracking interface achieve comparable performance to a traditional remote control interface when completing a drone racing task. Ten participants completed flight tests in which they navigated a drone through a racing track in a Gazebo simulation environment. Users reduced drone trajectory length by 18% (73.44 m vs. 89.29 m) using the eye-tracking interface to navigate racing gates effectively. The time taken to complete the route using the eye-tracking method (average of 70.01 seconds) was only 3.5% slower than using the remote control method (also average of 70.01 seconds), indicating the good efficiency of the interface. It is also worth mentioning that four of the participants completed the race with an average time that was 25.9% faster than the other participants. In addition, users evaluated highly the performance (M = 34.0, SD = 14.2) and low frustration (M = 30.5, SD = 9.2) with the eye-tracking interface compared to performance (M = 63.0, SD = 10.1) and frustration (M = 49.0, SD = 11.7) with the baseline remote controller. The hedonic quality (M = 1.65, SD = 0.45) was also evaluated high by the users in the UEQ questionnaire.

This paper introduces an innovative methodology for object manipulation on the surface of water through the collaboration of an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) connected to the object by tethers. We propose a novel mathematical model of a robotic system that combines the UAV, USV, and the tethered floating object. A novel Model Predictive Control (MPC) framework is designed for using this model to achieve precise control and guidance for this collaborative robotic system. Extensive simulations in the realistic robotic simulator Gazebo demonstrate the system's readiness for real-world deployment, highlighting its versatility and effectiveness. Our multi-robot system overcomes the state-of-the-art single-robot approach, exhibiting smaller control errors during the tracking of the floating object's reference. Additionally, our multi-robot system demonstrates a shorter recovery time from a disturbance compared to the single-robot approach.

A pair of lion brothers have made the longest swim ever recorded for their species – about 1.5 kilometres across hippo and crocodile-infested waters. The massive swim – equivalent to the aquatic leg of an Olympic triathlon – was the pair's fourth attempt to cross the Kazinga Channel in Queen Elizabeth National Park, Uganda, and was recorded by a drone-mounted thermal camera at night. The lions had to abort earlier attempts after encountering large animals, most likely hippos or Nile crocodiles, which are also visible in the footage. Making the effort even more extraordinary, one of the lions, named Jacob, has only three legs. Jacob has had an extremely challenging life, says Alexander Braczkowski at Griffith University in Australia: he has been gored by a buffalo, his family was poisoned for the lion body-part trade, he was caught in a poacher's snare and he eventually lost his leg after it was stuck in a poacher's steel trap.

Transportation missions in aerospace are limited to the capability of each aerospace robot and the properties of the target transported object, such as mass, inertia, and grasping locations. We present a novel decentralized adaptive controller design for multiple robots that can be implemented in different kinds of aerospace robots. Our controller adapts to unknown objects in different gravity environments. We validate our method in an aerial scenario using multiple fully actuated hexarotors with grasping capabilities, and a space scenario using a group of space tugs. In both scenarios, the robots transport a payload cooperatively through desired three-dimensional trajectories. We show that our method can adapt to unexpected changes that include the loss of robots during the transportation mission.

The integration of automation and voice control in drone systems has received significant attention in recent research, driven by the need for more intuitive and efficient human-machine interaction [4, 1]. This project focuses on developing a voice command system for the Tello drone, utilizing speech recognition and deep learning models to translate voice commands into precise drone actions. The primary challenge addressed by this project is the accurate and efficient translation of voice commands into specific drone operations. This is particularly crucial in scenarios where traditional control interfaces are impractical or where operators require hands-free operation [10, 5]. To address this challenge, we developed and evaluated three distinct pipelines. The first pipeline uses a traditional Speech-to-Text (STT) model followed by a Large Language Model (LLM) for command interpretation [11]. The second pipeline involves a direct mapping model that predicts drone commands from audio inputs without intermediate text conversion. The third pipeline employs a Siamese neural network to generalize new commands by comparing audio inputs to pre-trained examples [8]. Each pipeline was designed to balance performance, flexibility, and ease of maintenance.