Increasing Endurance of an Autonomous Robot using an Immune-Inspired Framework

This paper describes the implementation of an online immune-inspired framework to help increase endurance of an autonomous robot. Endurance is defined as the ability of the robot to exert itself for a long period of time. The immune-inspired framework provides such capability by monitoring the behavior of the robot to ensure continuous and safe behavior. The immune-inspired framework combines innate and adaptive immune inspired algorithms. Innate uses a dendritic cell based innate immune algorithm, and adaptive uses an instance based B-cell approach. Results presented in this paper shows that when the robot is implemented with the immune-inspired framework, health and survivability of a robot is improved, therefore increasing its endurance

Adaptive and Online Health Monitoring System for Autonomous Aircraft

Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour based on the progression of its sensory values produced by the aircraft's modules, components and/or subsystems. The AHMS indicates erroneous flight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are affecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer flight behaviour or fault tolerance to the aircraft. This allows the aircraft to fly safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)

This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 352)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects

Nanorobotics offers an emerging frontier in biomedicine, holding the potential to revolutionize diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, resulting in the identification and analysis of a total of 414 papers. The studies were filtered to include only those that addressed both nanorobotics and direct medical applications. Our analysis traces the technology's evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications over time. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of the state of nanorobotics in medicine, drawing attention to current challenges and opportunities, and providing directions for future research in this rapidly advancing field

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment

Design of an Autonomous Hovering Miniature Air Vehicle as a Flying Research Platform

This thesis, by developing a Miniature Aerial Vehicle (MAV) hovering platform, presents a practical solution to allow researchers and students to implement their theoretical methods for guidance and navigation in the real world. The thesis is not concerned with the development of guidance and navigation algorithms, nor is it concerned with the development of external sensors. There have been some recent advances in guidance and navigation towards developing algorithms and simple sensors for MAVs. The task of developing a platform to test such advancements is the subject of this thesis. It is considered a difficult and time consuming process due to the complexities of autonomous flight control and the strict size, weight and computational requirements of this type of system. It would be highly beneficial to be able to buy a platform specifically designed for this task that already possesses autonomous hovering capability and the expansion connectivity for interfacing your own custom developed sensors and algorithms. Many biological and computer scientists would jump at the opportunity to maximize their research by real world implementation. The development of such a system is not a trivial task. It requires a great deal of understanding in a broad range of fields including; Aeronautical, Microelectronic, Mechanical, Computer and Embedded Software Engineering in order to create a successful prototype. The challenge of this thesis was to design a research platform to enable easy implementation of external sensors and guidance algorithms, in a real world environment for research and education. The system is designed so it could be used for a broad range of testing experiments. After extensive research in current MAV and avionics design it became obvious in several areas the best available products were not sufficient to meet the needs of the proposed platform. Therefore it was necessary to custom design and build; sensors, a data acquisition system and a servo controller. The latter two products are available for sale by Jimonics (www.jimonics.com). It was then necessary to develop a complete flight control system with integrated sensors, processor and wireless communications network which is called ‘The MicroBrain’. ‘The MicroBrain’ board measures only 45mm x 35mm x 11mm and weighs ~11 grams. The coaxial contra-rotating MAV platform design provides a high level of mechanical stability to help minimise the control system complexity. The platform was highly modified from a commercially available remotely controlled helicopter. The system incorporates a novel collision protection system that was designed to also double as a mounting place for external sensors around its perimeter. The platform equipped with ‘The MicroBrain’ is capable of fully autonomous hover. This provides a great base for testing guidance and navigational sensors and algorithms by decoupling the difficult task of platform design and low-level stability control. By developing a platform with these capabilities the researcher can now focus on the guidance and navigation task, as the difficulties in developing a custom platform have been taken care of. This therefore promotes a faster evolution of guidance and navigational control algorithms for MAVs

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm