Leader-Follower Control and Distributed Communication based UAV Swarm Navigation in GPS-Denied Environment

Unmanned Aerial Vehicles (UAVs) have developed rapidly in recent years due to technological advances and UAV technology finds applications in a wide range of fields, including surveillance, search and rescue, and agriculture. The utilization of UAV swarms in these contexts offers numerous advantages, increasing their value across different industries. These advantages include increased efficiency in tasks, enhanced productivity, greater safety, and the higher data quality. The coordination of UAVs becomes particularly crucial during missions in these applications, especially when drones are flying in close proximity as part of a swarm. For instance, if a drone swarm is targeted or needs to navigate through a Global Positioning System (GPS)-denied environment, it may encounter challenges in obtaining the location information typically provided by GPS. This poses a new challenge for the UAV swarms to maintain a reliable formation and successfully complete a given mission. In this article, our objective is to minimize the number of sensors required on each UAV and reduce the amount of information exchanged between UAVs. This approach aims to ensure the reliable maintenance of UAV formations with minimal communication requirements among UAVs while they follow predetermined trajectories during swarm missions. In this paper, we introduce a concept that utilizes extended Kalman filter, leader-follower-based control and a distributed data-sharing scheme to ensure the reliable and safe maintenance of formations and navigation autonomously for UAV swarm missions in GPS-denied environments. The formation control approaches and control strategies for UAV swarms are also discussed

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

Material-integrated Prediction, Control, and Distributed Learning in Soft Robots

Soft roboticists promise lofty goals around soft robots, such as Mars, deep-sea, and internal human body exploration. Yet, roboticists design and build algorithms that make the robot reliant on personal computers or clouds for important functions. In most soft robotic designs, a low-power microcontroller unit (MCU) collects sensor signals and sends actuation commands from the material. This work focuses on making the first steps towards computationally untethering advanced prediction, control, and learning algorithms onto the already existing MCUs in the soft robot material by considering the algorithm&rsquo;s memory needs and power consumption. The first step towards this work is the proposal, building, deployment, and open-sourcing of a compiler (nn4mc, https://nn4mc.com) that allows researchers and hobbyists to translate neural network inference trained in Tensorflow into C code that is interpretable by generalized low-power MCUs. The second step consists of untethering real-time high-bandwidth nonlinear control that uses neu�ral network forward kinematic models compiled using nn4mc and real-time optimization toward nonlinear predictive control that consumes less than 46mW of average power and controls soft ac�tuators with less than 2 mm of error for a path following task. The third step consists of encoding sensor signals from the soft robot onto a space where active learning becomes a trivial task through observation embeddings and graph-based distributed semi-supervised learning without the need for backpropagation at the low power memory-restricted compute units in a simulated study. nn4mc has sparked collaborations with many researchers, such as real-time prediction in prosthetic fin�gertips, aeroelastic-aware airplane wings, and real-time pose classification on a wearable garment worn by drag queens. Taken together, this body of work presents tools and methods to deliver on the promise of computationally untethered soft robots; that is, accessible firmware engineering tractable at low-power compute, which belongs in the future of soft robotics.</p