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

Formation morphing and collision avoidance in swarms of robots

Formation maintenance and collision avoidance are two of the key factors in swarm robotics. The demand for autonomous fleets of robots is ever increasing from manufacturing to product deliveries to surveillance to mapping and so on. Moreover, for resource constrained autonomous robots, such as UAVs and UGVs, energy-efficiency is very vital due to their limited batteries. Therefore formation maintenance and collision avoidance developed for such robots need to be energy-efficient. Integration between these two approaches needs to be performed systematically. The experimental analysis of the proposed approaches presented in this thesis target two main branches: 1) action based and 2) perception based energy consumption in a swarm of robots. In the first branch, there are two different paths: i) optimal formation morphing: the main goal is to the optimize the reformation process from the highest level of agitation of the swarm, i.e., maximum disturbance in the formation shape and ii) congestion minimization: the main goal here is to find an optimal solution for distribution of the swarm into sub-swarms to minimize the delays due to over population of the agents while bypassing the obstacles. In the second branch, i.e., perception based energy consumption, the main goal is to increase the mission life on a single charge by injecting the adaptive consciousness into the agents so they can turn off their ranging sensors and navigate while listening to their leader. For formation collision co-awareness, we systematically integrated the methodologies by designing a multi-priority control and utilized the non-rigid mapping scheme of thin-plate splines technique to minimize the deformation caused by obstacle avoidance. For congestion-aware morphing and avoidance maneuvers, we discuss how the delays caused by over population can be minimized with local sense and avoid approach. The leader, upon detection of obstacles, pre-estimates the optimal configuration, i.e., number of agents in the sub-swarms, and divides the swarm as such. We show the efficiency of the proposed approach experimentally

Pause-and-Go Self-Balancing Formation Control of Autonomous Vehicles Using Vision and Ultrasound Sensors

In this work, we implement a decentralized and noncooperative state estimation and control algorithm to autonomously balance a team of robots in a circular formation pattern. The group of robots includes a leader periodically moving at a constant steering angle and a set of followers that, by only leveraging intermittent and noisy proximity measurements, independently implement a fully decentralized state estimation control algorithm to determine and adjust their relative position with closest neighbors. The algorithm is conducted in a pause-and-go sequence, where, during the pause, each robot stops to gather and process the information coming from the measurements, estimate the relative phase with respect to the others, and identify its closest pursuant. During the go, each robot accelerates to space from its closest pursuant and then to move at a constant speed when the desired spacing is achieved. The algorithm is tested in an unprecedented experiment on a custom-made low-cost caster-wheeled robotic framework featuring sonar and vision sensors mounted on a rotating platform to estimate the proximity distance to closer neighbors. The control scheme, which does not necessitate cooperation and is capable of coping with uncertain and intermittent sensor feedback data, is shown to be effective in balancing the robot on the circle even when, at a steady state, no feedback sensor data are available

Trajectory Optimization of Meteorological Sampling

Swarming involves controlling multiple unmanned aerial systems or UAS in formation through the use of controls and algorithms. Swarm systems may be distributed and not rely on a central controller. As a result, this gives the system the potential to be robust and scalable, allowing for flexibility for the engineers to approach problems differently. Based on a variety of a few models and algorithms, such as artificial potential fields (APFs), agent-based modeling, dynamic data driven application systems (DDDAS), and virtual structures, it may be determined that using a variation of one of these would be the best course of action for formation flight for a swarm of UASs. Choosing the right controller is dependent on what works best for acquiring atmospheric data in a coordinated formation. Current atmospheric data is commonly taken using a weather tower or mesonet. A mesonet is typically a 10m high tower with a pressure, temperature, humidity sensor placed at the top. Deciding which controller can be used to not only take useful atmospheric data, but in many cases replace a mesonet due to mobility and customization is the goal. A wind profile is a transient matter, so using a swarm vs using one drone or a mesonet helps to solve the issues that the latter two run into due to time and space. A swarm can record multiple points at one time due to each agent being a data point representation, whereas a single drone can only account for a single location in time. A swarm using a virtual structure (VS) can cover a variety of amounts of space in a coordinated shape. A meosnet is stationary and only oriented vertically and an uncoordinated group of UAS does not have the capability to operate together. This leaves the capability that a VS swarm has to fill in the gaps or even replace the traditional approaches. An array of sensor packages with mobility, coordinated movement, and endless data points could give the VS swarm the advantage in atmospheric data sampling

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

UAV group formation collision avoidance method based on second-order consensus algorithm and improved artificial potential field

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the "leader-follower" strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method

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