Establishing and optimising unmanned airborne relay networks in urban environments

This thesis assesses the use of a group of small, low-altitude, low-power (in terms of communication equipment), xed-wing unmanned aerial vehicles (UAVs) as a mobile communication relay nodes to facilitate reliable communication between ground nodes in urban environments. This work focuses on enhancing existing models for optimal trajectory planning and enabling UAV relay implementation in realistic urban scenarios. The performance of the proposed UAV relay algorithms was demonstrated and proved through an indoor simulated urban environment, the rst experiment of its kind.The objective of enabling UAV relay deployment in realistic urban environments is addressed through relaxing the constraints on the assumptions of communication prediction models assumptions, reducing knowledge requirements and improving prediction efficiency. This thesis explores assumptions for urban environment knowledge at three different levels: (i) full knowledge about the urban environment, (ii) partially known urban environments, and (iii) no knowledge about the urban environment. The work starts with exploring models that assume the city size, layout and its effects on wireless communication strength are known, representing full knowledge about the urban environment. [Continues.]</div

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Investigations in multi-resolution modelling of the quadrotor micro air vehicle

Multi-resolution modelling differs from standard modelling in that it employs multiple abstractions of a system rather than just one. In describing the system at several degrees of resolution, it is possible to cover a broad range of system behaviours with variable precision. Typically, model resolution is chosen by the modeller, however the choice of resolution for a given objective is not always intuitive. A multi-resolution model provides the ability to select optimal resolution for a given objective. This has benefits in a number of engineering disciplines, particularly in autonomous systems engineering, where the behaviours and interactions of autonomous agents are of interest. To investigate both the potential benefits of multi-resolution modelling in an autonomous systems context and the effect of resolution on systems engineering objectives, a multi-resolution model family of the quadrotor micro air vehicle is developed. The model family is then employed in two case studies. First, non-linear dynamic inversion controllers are derived from a selection of the models in the model family, allowing the impact of resolution on a model-centric control strategy to be investigated. The second case study employs the model family in the optimisation of trajectories in a wireless power transmission. This allows both study of resolution impact in a multi-agent scenario and provides insight into the concept of laser-based wireless power transmission. In addition to the two primary case studies, models of the quadrotor are provided through derivation from first principles, system identification experiments and the results of a literature survey. A separate model of the quadrotor is employed in a state estimation experiment with low-fidelity sensors, permitting further discussion of both resolution impact and the benefits of multi-resolution modelling. The results of both the case studies and the remainder of the investigations highlight the primary benefit of multi-resolution modelling: striking the optimal balance between validity and efficiency in simulation. Resolution is demonstrated to have a non-negligible impact on the outcomes of both case studies. Finally, some insights in the design of a wireless power transmission are provided from the results of the second case study

Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload

In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load path tracking performance for single and two quadrotors with a cable slung load. The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model. Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were tested in MATLAB simulation and verified for various desired predefined trajectories. In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points

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

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

Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work