Low computational SLAM for an autonomous indoor aerial inspection vehicle

The past decade has seen an increase in the capability of small scale Unmanned Aerial Vehicle (UAV) systems, made possible through technological advancements in battery, computing and sensor miniaturisation technology. This has opened a new and rapidly growing branch of robotic research and has sparked the imagination of industry leading to new UAV based services, from the inspection of power-lines to remote police surveillance. Miniaturisation of UAVs have also made them small enough to be practically flown indoors. For example, the inspection of elevated areas in hazardous or damaged structures where the use of conventional ground-based robots are unsuitable. Sellafield Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require frequent safety inspections. UAV inspections eliminate the current risk to personnel of radiation exposure and other hazards in tall structures where scaffolding or hoists are required. This project focused on the development of a UAV for the novel application of semi-autonomously navigating and inspecting these structures without the need for personnel to enter the building. Development exposed a significant gap in knowledge concerning indoor localisation, specifically Simultaneous Localisation and Mapping (SLAM) for use on-board UAVs. To lower the on-board processing requirements of SLAM, other UAV research groups have employed techniques such as off-board processing, reduced dimensionality or prior knowledge of the structure, techniques not suitable to this application given the unknown nature of the structures and the risk of radio-shadows. In this thesis a novel localisation algorithm, which enables real-time and threedimensional SLAM running solely on-board a computationally constrained UAV in heavily cluttered and unknown environments is proposed. The algorithm, based on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour searches and point-cloud decimation to reduce the processing requirements has successfully been tested in environments similar to that specified by Sellafield Ltd

Multi-robot Collaborative Visual Navigation with Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs), particularly multi-rotor MAVs have gained significant popularity in the autonomous robotics research field. The small size and agility of these aircraft makes them safe to use in contained environments. As such MAVs have numerous applications with respect to both the commercial and research fields, such as Search and Rescue (SaR), surveillance, inspection and aerial mapping. In order for an autonomous MAV to safely and reliably navigate within a given environment the control system must be able to determine the state of the aircraft at any given moment. The state consists of a number of extrinsic variables such as the position, velocity and attitude of the MAV. The most common approach for outdoor operations is the Global Positioning System (GPS). While GPS has been widely used for long range navigation in open environments, its performance degrades significantly in constrained environments and is unusable indoors. As a result state estimation for MAVs in such constrained environments is a popular and exciting research area. Many successful solutions have been developed using laser-range finder sensors. These sensors provide very accurate measurements at the cost of increased power and weight requirements. Cameras offer an attractive alternative state estimation sensor; they offer high information content per image coupled with light weight and low power consumption. As a result much recent work has focused on state estimation on MAVs where a camera is the only exteroceptive sensor. Much of this recent work focuses on single MAVs, however it is the author's belief that the full potential and benefits of the MAV platform can only be realised when teams of MAVs are able to cooperatively perform tasks such as SaR or mapping. Therefore the work presented in this thesis focuses on the problem of vision-based navigation for MAVs from a multi-robot perspective. Multi-robot visual navigation presents a number of challenges, as not only must the MAVs be able to estimate their state from visual observations of the environment but they must also be able to share the information they gain about their environment with other members of the team in a meaningful fashion. The meaningful sharing of observations is achieved when the MAVs have a common frame of reference for both positioning and observations. Such meaningful information sharing is key to achieving cooperative multi-robot navigation. In this thesis two main ideas are explored to address these issues. Firstly the idea of appearance based (re)-localisation is explored as a means of establishing a common reference frame for multiple MAVs. This approach allows a team of MAVs to very easily establish a common frame of reference prior to starting their mission. The common reference frame allows all subsequent operations, such as surveillance or mapping, to proceed with direct cooperative between all MAVs. The second idea focuses on the structure and nature of the inter-robot communication with respect to visual navigation; the thesis explores how a partially distributed architecture can be used to vastly improve the scalability and robustness of a multi-MAV visual navigation framework. A navigation framework would not be complete without a means of control. In the multi-robot setting the control problem is complicated by the need for inter-robot collision avoidance. This thesis presents a MAV trajectory controller based on a combination of classical control theory and distributed Velocity Obstacle (VO) based collision avoidance. Once a means of control is established an autonomous multi-MAV team requires a mission. One such mission is the task of exploration; that is exploration of a previously unknown environment in order to produce a map and/or search for objects of interest. This thesis also addressed the problem of multi-robot exploration using only the sparse interest-point data collected from the visual navigation system. In a multi-MAV exploration scenario the problem of task allocation, assigning areas to each MAV to explore, can be a challenging one. An auction-based protocol is considered to address the task allocation problem. The two applications discussed, VO-based trajectory control and auction-based environment exploration, form two case studies which serve as the partial basis of the evaluation of the navigation solutions presented in this thesis. In summary the visual navigation systems presented in this thesis allow MAVs to cooperatively perform task such as collision avoidance and environment exploration in a robust and efficient manner, with large teams of MAVs. The work presented is a step in the direction of fully autonomous teams of MAVs performing complex, dangerous and useful tasks in the real world

Stochastic Real-time Optimal Control for Bearing-only Trajectory Planning

A method is presented to simultaneously solve the optimal control problem and the optimal estimation problem for a bearing-only sensor. For bearing-only systems that require a minimum level of certainty in position relative to a source for mission accomplishment, some amount of maneuver is required to measure range. Traditional methods of trajectory optimization and optimal estimation minimize an information metric. This paper proposes constraining the final value of the information states with known time propagation dynamics relative to a given trajectory which allows for attainment of the required level of information with minimal deviation from a general performance index that can be tailored to a specific vehicle. The proposed method does not suffer from compression of the information metric into a scalar, and provides a route that will attain a particular target estimate quality while maneuvering to a desired relative point or set. An algorithm is created to apply the method in real-time, iteratively estimating target position with an Unscented Kalman Filter and updating the trajectory with an efficient pseudospectral method. Methods and tools required for hardware implementation are presented that apply to any real-time optimal control (RTOC) system. The algorithm is validated with both simulation and flight test, autonomously landing a quadrotor on a wire

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

Autonomous, Collaborative, Unmanned Aerial Vehicles for Search and Rescue

Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue

A Framework for Site-Specific Spatial Audio Applications

As audio recording and reproduction technology has advanced over the past five decades, increasing attention has been paid to recreating the highly spatialised listening experience we understand from our physical environment. This is the logical next step in the quest for increasing audio clarity, particularly as virtual reality gaming and augmented reality experiences become more widespread. This study sought to develop and demonstrate a technical framework for the production of site-specific audio-based works that is user-friendly and cost effective. The system was intended to be used by existing content producers and audio programmers to work collaboratively with a range of site-based organisations such as museums and galleries to produce an audio augmentation of the physicality of the space. This research was guided by four key aims: 1. Demonstrate a compositional method for immersive spatial audio that references the novel physical environment and the listener’s movement within it. 2. Describe a framework for the development and deployment of a spatial audio visitor technology system. 3. Prototype a naturalistic method for the delivery and navigation of contextual information via audio. 4. Deploy, demonstrate, and evaluate a spatial audio experience within a representative environment. The resulting system makes use of a range of existing technologies to provide a development experience and output that meets a clearly defined set of criteria. Furthermore, a case study application has been developed that demonstrates the use of the system to augment a selection of six paintings in a gallery space. For each of these paintings, a creative spatial composition was produced that demonstrates the principles of spatial composition discussed in this thesis. A spoken informational layer sits on top of this acting as a museum audio guide, featuring navigation using head gestures for a hands-free experience. This thesis presents a detailed discussion of the artistic intentions and techniques employed in the production of the six soundscapes, as well as an evaluation of the resulting application in use in a public gallery space