Pheromone-based Swarming for Position-less MAVs

Unlike existing aerial swarm systems, we aim at developing algorithms which do not require global or relative positioning information concerning agents and their neighbors. This alleviates the need for sensors which require calibration, are expensive and heavy or unusable because of environmental constraints. Rather than positioning, MAVs rely only on simple sensors (magnetic compass, speed sensor and altitude sensor) and local communication with neighbors. Our endeavor is motivated by an application whereby Micro Air Vehicles (MAVs) must organize autonomously to establish a robust communication network between users located on ground. Such a system is aimed towards the rapid and easy deployment of communication networks in disaster areas

Evolved Navigation Control for Unmanned Aerial Vehicles

Whether evolutionary robotics (ER) controllers evolve in simulation or on real robots, realworld performance is the true test of an evolved controller. Controllers must overcome the noise inherent in real environments to operate robots efficiently and safely. To prevent a poorly performing controller from damaging a vehicle—susceptible vehicles includ

Sensitivity analysis of a relative navigation solution for unmanned aerial vehicles in a GNSS-denied environment

Cooperative navigation between two or more unmanned aerial vehicles (UAVs) is an important enabling technology for problems such as military reconnaissance, disaster response, and search and rescue. In many of these situations Global Navigation Satellite Systems (GNSS), such as Global Positioning System (GPS), may be unreliable or unavailable due to structural impedance or malicious signal jamming. Therefore, the task of maintaining a reliable relative navigation solution without the use of GNSS is an important need for the aforementioned missions.;To meet this need, this thesis focuses on the relative navigation between two UAVs that are operating in a GNSS-denied environment. In particular, the design and sensitivity of a navigation algorithm are presented. The navigation algorithm presented consists of an Unscented Kalman filter that fuses multiple on-board sensors to estimate the relative pose between two UAVs. These sensors include: strap-down inertial measurement units, ultra-wideband ranging radios, strap-down tri-axial magnetometers, and downward facing cameras. Through the use of a Monte Carlo simulation study, the presented algorithm\u27s performance sensitivity to various sensor payload characteristics, flight dynamics, and initial condition errors is evaluated. Additionally, a research platform that will provide for a future experimental evaluation of the algorithm presented in this thesis has been integrated and tested as part of this work

Ant-based Swarming with Positionless Micro Air Vehicles for Communication Relay

Swarming without positioning information is interesting in application- oriented systems because it alleviates the need for sensors which are dependent on the environment, expensive in terms of energy, cost, size and weight, or unusable at useful ranges for real-life scenarios. This principle is applied to the development of a swarm of micro air vehicles (SMAVs) for the deployment of ad hoc wireless communication networks (SMAVNETs) between ground users in disaster areas. Rather than relying on positioning information, MAVs rely on local communication with immediate neighbors and proprioceptive sensors which provide heading, speed and altitude. To solve the challenging task of designing agent controllers to achieve the swarm behavior of the SMAVNET, inspiration is taken from army ants which are capable of laying and maintaining pheromone paths leading from their nest to food sources in nature. This is analogous to the deployment of communication pathways between multiple ground users. However, instead of being physically deposited in the air or on a map, pheromone is virtually deposited on the MAVs using local communication. This approach is investigated in 3D simulation in a simplified scenario with two ground users

Development of an Experimental Platform for Testing Autonomous UAV Guidance and Control Algorithms

With the United States\u27 push towards using unmanned aerial vehicles (UAVs) for more military missions, wide area search theory is being researched to determine the viability of multiple vehicle autonomous searches over the battle area. Previous work includes theoretical development of detection and attack probabilities while taking into account known enemy presence within the search environment. Simulations have been able to transform these theories into code to predict the UAV performance against known numbers of true and false targets. The next step to transitioning these autonomous search algorithms to an operational environment is the experimental testing of these theories through the use of surrogate vehicles, to determine if the guidance and control laws developed can guide the vehicles when operating in search areas with true and false targets. In addition to the challenge of experimental implementation, dynamic scaling must also be considered so that these smaller surrogate vehicles will scale to full size UAVs performing searches in real world scenarios. This research demonstrates the ability of a given sensor to use a basic ATR algorithm to identify targets in a search area based on its size and color. With this ability, the system\u27s target thresholds can also be altered to mimic real world UAV sensor performance

Improving Navigation Through Cooperation and Path Planning

The ability to reliably estimate own-states is very important for Unmanned Aerial Vehicles (UAVs) in executing their missions. Most current approaches for UAV state estimation rely on fusing inertial information from accelerometers and gyroscopes with absolute position information from a position sensor. Global Positioning System (GPS) is one of the most widely used position sensors. However, GPS signals are not reliable, and can be jammed by adversarial forces. Without the aid of an absolute position reference such as GPS the navigation solution of the system is going to drift with time. The problem of two autonomous vehicles traveling in a two dimensional environment from an initial location to a known goal location without any absolute position reference is considered. The effect of cooperation between the vehicles by considering the measurements such as relative range to help in improving the navigation state estimation and its effect on the observability of the system is discussed. The reduction in the navigation solution drift of the system, with cooperation between the agents, using measured relative information and its effect on the observability of the system while taking different paths is discussed. Simulations and theoretical results show that relative motion between the agents helps reduce the navigation drift of the agents when there is no absolute position reference.Mechanical & Aerospace Engineerin

Aerial collective systems

Deployment of multiple flying robots has attracted the interest of several research groups in the recent times both because such a feat represents many interesting scientific challenges and because aerial collective systems have a huge potential in terms of applications. By working together, multiple robots can perform a given task quicker or more efficiently than a single system. Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases. Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume. Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours. Flying in formation provides an effective way of decongesting the airspace. Aerial swarms also have an enormous artistic potential because they allow creating physical 3D structures that can dynamically change their shape over time. However, the challenges to actually build and control aerial swarms are numerous. First of all, a flying platform is often more complicated to engineer than a terrestrial robot because of the inherent weight constraints and the absence of mechanical link with any inertial frame that could provide mechanical stability and state reference. In the first section of this chapter, we therefore review this challenges and provide pointers to state-of-the-art methods to solve them. Then as soon as flying robots need to interact with each other, all sorts of problems arise such as wireless communication from and to rapidly moving objects and relative positioning. The aim of section 3 is therefore to review possible approaches to technically enable coordination among flying systems. Finally, section 4 tackles the challenge of designing individual controllers that enable a coherent behavior at the level of the swarm. This challenge is made even more difficult with flying robots because of their 3D nature and their motion constraints that are often related to the specific architectures of the underlying physical platforms. In this third section is complementary to the rest of this book as it focusses only on methods that have been designed for aerial collective systems

Opportunistic communication schemes for unmanned vehicles in urban search and rescue

In urban search and rescue (USAR) operations, there is a considerable amount of danger faced by rescuers. The use of mobile robots can alleviate this issue. Coordinating the search effort is made more difficult by the communication issues typically faced in these environments, such that communication is often restricted. With small numbers of robots, it is necessary to break communication links in order to explore the entire environment. The robots can be viewed as a broken ad hoc network, relying on opportunistic contact in order to share data. In order to minimise overheads when exchanging data, a novel algorithm for data exchange has been created which maintains the propagation speed of flooding while reducing overheads. Since the rescue workers outside of the structure need to know the location of any victims, the task of finding their locations is two parted: 1) to locate the victims (Search Time), and 2) to get this data outside the structure (Delay Time). Communication with the outside is assumed to be performed by a static robot designated as the Command Station. Since it is unlikely that there will be sufficient robots to provide full communications coverage of the area, robots that discover victims are faced with the difficult decision of whether they should continue searching or return with the victim data. We investigate a variety of search techniques and see how the application of biological foraging models can help to streamline the search process, while we have also implemented an opportunistic network to ensure that data are shared whenever robots come within line of sight of each other or the Command Station. We examine this trade-off between performing a search and communicating the results

Swarm-inspired solution strategy for the search problem of unmanned aerial vehicles

Learning from the emergent behaviour of social insects, this research studies the influences of environment to collective problem-solving of insect behaviour and distributed intelligent systems. Literature research has been conducted to understand the emergent paradigms of social insects, and to investigate current research and development of distributed intelligent systems. On the basis of the literature investigation, the environment is considered to have significant impact on the effectiveness and efficiency of collective problem-solving. A framework of collective problem-solving is developed in an interdisciplinary context to describe the influences of the environment to insect behaviour and problem-solving of distributed intelligent systems. The environment roles and responsibilities are transformed into and deployed as a problem-solving mechanism for distributed intelligent systems. A swarm-inspired search strategy is proposed as a behaviour-based cooperative search solution. It is applied to the cooperative search problem of Unmanned Aerial Vehicles (UAVs) with a series of experiments implemented for evaluation. The search environment represents the specification and requirements of the search problem; defines tasks to be achieved and maintained; and it is where targets are locally observable and accessible to UAVs. Therefore, the information provided through the search environment is used to define rules of behaviour for UAVs. The initial detection of target signal refers to modified configurations of the search environment, which mediates local communications among UAVs and is used as a means of coordination. The experimental results indicate that, the swarm-inspired search strategy is a valuable alternative solution to current approaches of cooperative search problem of UAVs. In the proposed search solution, the diagonal formation of two UAVs is able to produce superior performance than the triangular formation of three UAVs for the average detection time and the number of targets located within the maximum time length