UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Hierarchical-Structure-Based Fault Estimation and Fault-Tolerant Control for Multiagent Systems

This paper proposes a hierarchical-structure-based fault estimation and fault-tolerant control design with bidirectional interactions for nonlinear multiagent systems with actuator faults. The hierarchical structure consists of distributed multiagent system hierarchy, undirected topology hierarchy, decentralized fault estimation hierarchy, and distributed fault-tolerant control hierarchy. The states and faults of the system are estimated simultaneously by merging the unknown input observer in a decentralized fashion. The distributed-constant-gain-based and node-based fault-tolerant control schemes are developed to guarantee the asymptotic stability and H-infinity performance of multiagent systems, respectively, based on the estimated information in the fault estimation hierarchy and the relative output information from neighbors. Two simulation cases validate the efficiency of the proposed hierarchical structure control algorithm

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

Multi-agent methodology for distributed and cooperative supervisory estimation subject to unreliable information

In this work, a novel multi-agent framework for cooperative supervisory estimation of linear time-invariant systems is proposed. This framework is developed based on the notion of subobservers and a discrete-event system (DES) supervisory control and is applicable to large-scale systems. We introduce a group of subobservers where each subobserver is estimating certain states that are conditioned on a given input, output and state information. The cooperation among the subobservers is managed by a DES supervisor. The supervisor makes decisions regarding the selection and configuration of a set of subobservers to successfully estimate all the system states, while the feasibility of the overall integrated cooperative subobservers is verified. When certain anomalies (faults) are present in the system, or the sensors and subobservers become unreliable, the supervisor reconfigures the set of selected subobservers so that the impacts of anomalies on the estimation performance are minimised to the extent that is possible. The application and capabilities of our proposed methodology in a practical industrial process is demonstrated through numerical simulations

Formation Control and Fault Accommodation for a Team of Autonomous Underwater Vehicles

The purpose of this thesis is the development of efficient formation control and fault accommodation algorithms for a team of autonomous underwater vehicles (AUVs). The team of AUVs are capable of performing a wide range of deep water marine applications such as seabed mapping and surveying, oil and gas exploration and extraction, and oil and gas pipeline inspection. However, communication limitations and the presence of undesirable events such as component faults in any of the team members can prevent the whole team to achieve safe, reliable, and efficient performance while executing underwater mission tasks. In this regard, the semi-decentralized control scheme is developed to achieve trajectory tracking and formation keeping while requiring information exchange only among neighboring agents. To this end, model predictive control (MPC) technique and dynamic game theory are utilized to formulate and solve the formation control problem. Moreover, centralized and decentralized control schemes are developed to assess the performance of the proposed semi-decentralized control scheme in the simulation studies. The simulation results verify that the performance of the proposed semi-decentralized scheme is very close to the centralized scheme with lower control effort cost while it does not impose stringent communication requirements as in the centralized scheme. Moreover, the semi-decentralized active fault recovery scheme is developed to maintain a graceful degraded performance and to ensure that the team of autonomous underwater vehicles can satisfy mission objectives when an actuator fault occurs in any of the team members. In this regard, online fault information provided by fault detection and isolation (FDI) modules of each agent and its neighbors are incorporated to redesign the nominal controllers based on the MPC technique and dynamic game theory. Additionally, FDI imperfections such as fault estimation error and time delay are taken into account, and a performance index is derived to show the impact of FDI imperfections on the performance of team members. Moreover, centralized and decentralized active fault recovery schemes are developed to evaluate the performance of the proposed semi-decentralized recovery scheme through comparative simulation studies with various fault scenarios. The comparative simulation studies justify that the proposed semi-decentralized fault recovery scheme meets the design specifications even if the performance of the FDI module is not ideal