SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

A Model based Safety Assessment for Multirotors

Unmanned Aerial Vehicles (UAVs) must be safe and reliable to prevent fatal accidents in densely populated areas. This research makes the first steps to create a framework which can integrate safety and reliability considerations in the design process. The conceptual design process should consider creating design models coupling sizing with system architecture. Additionally, the multirotor has safety challenges from the propulsor configuration. They lose flight control and show erroneous flight behaviour when propulsors fail. Hence, the design models of multirotor should also incorporate a controllability assessment method to identify and isolate uncontrollable events. For this matter, an appropriate tool should be considered to create such design models. A combination of OpenAltarica, System Analyst and Python is used to create design models of multirotor in a model-based safety assessment framework. These models are developed by integrating system architecture and controllability assessment following the etiquettes of the process. A case study is used to validate the framework and to demonstrate its ability to explore innovative designs. The reliability analysis confirms that the multirotors are fault-tolerant except quadrotor and some configurations are potentially highly reliable. The results demonstrate the feasibility of the multirotor system modelling methods in terms of reliability and pave the way to further develop the model-based safety assessment framework with sizing methodologies. The models can also be further enhanced with the addition of a component fault library, additional failure modes and implementation of diagnosability analysis, fault detection and identification analysis. Fault libraries and failure modes can help in foreseeing uncontrollable cases. In contrast, diagnosability analysis, fault detection and identification analysis can integrate detect, isolate and recover mechanisms, and ensure redundancy optimization effectively. Additionally, the framework should also be combined with multidisciplinary design optimization for sizing. Such design models can contribute to the emergence of UAVs for safety-critical applications

Product Development Process for Small Unmanned Aerial Systems

The DoD has recognized the need for persistent Intelligence, Surveillance and Reconnaissance (ISR) over the last two decades. Recent developments with commercial drones have changed the market structure; there is now a thriving and extensive market base for drone based remote sensing. This research provides system engineering methods to support the DoD use of this burgeoning market to meet operational ISR needs. The three contributions of this research are: a process to support Small Unmanned Aerial Systems (SUAS) design, tools to support the design process, and tools to support risk assessment and reduction for both design and operations. The process and tools are presented via an exemplar design for an ISR SUAS mission. The exemplar design flows from user needs through to an allocated baseline with an assessment of system reliability based on a compilation of commercial component reliability and failure modes

Aeronautical Engineering: A continuing bibliography (supplement 158)

This bibliography lists 499 reports, articles and other documents introduced into the NASA scientific and technical information system in January 1983

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios

Adaptive UAV swarm mission planning by temporal difference learning

The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements

Throughput Maximization in Unmanned Aerial Vehicle Networks

The use of Unmanned Aerial Vehicles (UAVs) swarms in civilian applications such as surveillance, agriculture, search and rescue, and border patrol is becoming popular. UAVs have also found use as mobile or portable base stations. In these applications, communication requirements for UAVs are generally stricter as compared to conventional aircrafts. Hence, there needs to be an efficient Medium Access Control (MAC) protocol that ensures UAVs experience low channel access delays and high throughput. Some challenges when designing UAVs MAC protocols include interference and rapidly changing channel states, which require a UAV to adapt its data rate to ensure data transmission success. Other challenges include Quality of Service (QoS) requirements and multiple contending UAVs that result in collisions and channel access delays. To this end, this thesis aims to utilize Multi-Packet Reception (MPR) technology. In particular, it considers nodes that are equipped with a Successive Interference Cancellation (SIC) radio, and thereby, allowing them to receive multiple transmissions simultaneously. A key problem is to identify a suitable a Time Division Multiple Access (TDMA) transmission schedule that allows UAVs to transmit successfully and frequently. Moreover, in order for SIC to operate, there must be a sufficient difference in received power. However, in practice, due to the location and orientation of nodes, the received power of simultaneously transmitting nodes may cause SIC decoding to fail at a receiver. Consequently, a key problem concerns the placement and orientation of UAVs to ensure there is diversity in received signal strength at a receiving node. Lastly, interference between UAVs serving as base station is a critical issue. In particular, their respective location may have excessive interference or cause interference to other UAVs; all of which have an impact on the schedule used by these UAVs to serve their respective users