Self-Maintenance

IET indexes its books and journal in SCOPUS and IEEE Xplore.
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications

The organizational account of biological functions interprets functions as contributions of a trait to the maintenance of the organization that, in turn, maintains the trait. As has been recently argued, however, the account seems unable to provide a unified grounding for both intra- and cross-generation functions, since the latter do not contribute to the maintenance of the same organization which produces them. To face this 'ontological problem', a splitting account has been proposed, according to which the two kinds of functions require distinct organizational definitions. In this article, we propose a solution for the ontological problem, by arguing that intra- and cross-generation functions can be said to contribute in the same way to the maintenance of the biological organization, characterized in terms of organizational self-maintenance. As a consequence, we suggest maintaining a unified organizational account of biological functions.

Two models of self-maintaining systems are presented. The equation of motion for
the surface of the system in the first chapter is a function only of the surface itself.
Moreover, for the sake of simplicity, I assume that this is a function of the local
mean curvature only. This equation of motion leads to a stationary shape of the
system (circle, sphere) with a determined size, but it also destabilizes the shape.
To counteract this fact, a term which behave like a surface tension is added.

The model of a self-maintaining system in the second chapter is based on reaction-diffusion
equations. Consider a system surrounded by a nutrient solution. This
system essentially consists of a building-material A, which I assume to be homogeneous
and incrompressible. The nutrient N is diffussing in- and outside the
system and has a constant concentration at infinity. Inside the system there are
two chemical reactions. One from the nutrient to the building-material and the
other from the building-material to a decay-material Z. The decay-material do I
not consider, cause it should leave the system quickly enough. This process leads
also to a stationary shape (circle, sphere) of the system with a determined size.
Again the surface tension stabilizes the shape.

Although the two models of self-maintaining systems look very different, they
have some common properties. If the surface tension is strong enough, the stationary
shape is stable. Decreasing the surface tension leads to an instability of
the surface. The dominant mode is ℓ=2. This means in two dimensions the trigonometrical
functions sin(2 φ) and cos(2 φ) and in three dimensions the spherical
harmonics Y₂,ₘ.

But what happens in the unstable region? The tools to answer this question are
the so-called bifurcation analysis (up to second and third order) and numerical
calculations of the model equations.

For both models there exists - in three dimensions - a certain parameter region
in which the instability drives the system to a division (like a cell division). But
for the model in the first chapter there exist other parameter regions, too, where
the instability drives the system to new stable shapes which look like oblate or
prolate spheres (like the shape of erythrocytes and rod-shaped bacteria).
It should be noted, that such models of self-maintainig systems, although seeming
oversimplified compared with real living cells, share some essential properties with
them.

In order to make a robot execute a given task plan more robustly we want to enable it to take care of its self-maintenance requirements during online execution of this plan. This requires the robot to know about the (internal) states of its components, constraints that restrict execution of actions and how to recover from faulty situations. The general idea is to implement a transformation process on the plans, which are specified in the agent programming language \ReadyLog{}, to be performed based on explicit qualitative temporal constraints. Afterwards, a 'guarded' execution of the transformed program results in more robust behavior.

In order to make a robot execute a given task plan more robustly we want to enable it to take care of its self-maintenance requirements during online execution of this program. This requires the robot to know about the (internal) states of its components, constraints that restrict execution of certain actions and possibly also how to recover from faulty situations. The general idea is to implement a transformation process on the plans, which are specified in the agent programming language ReadyLog, to be performed based on explicit (temporal) constraints. Afterwards, a 'guarded' execution of the transformed program should result in more robust behavior.