Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

Direct method of finding first integral of two-dimensional autonomous systems in polar coordinates

A direct method to find the first integral for two-dimensional autonomous system in polar coordinates is suggested. It is shown that if the equation of motion expressed by differential 1-forms for a given autonomous Hamiltonian system is multiplied by a set of multiplicative functions, then the general expression of the first integral can be obtained, An example is given to illustrate the application of the results.

A necessary and sufficient condition for transforming autonomous systems into linear autonomous Birkhoffian systems

The problem of transforming autonomous systems into Birkhoffian systems is studied. A reasonable form of linear autonomous Birkhoff equations is given. By combining them with the undetermined tensor method, a necessary and sufficient condition for an autonomous system to have a representation in terms of linear autonomous Birkhoff equations is obtained. The methods of constructing Birkhoffian dynamical functions are given. Two examples are given to illustrate the application of the results.

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

The Existence of Periodic Orbits for Higher Dimensional Autonomous Systemswith Small Perturbations

By means of theory of toplogical degree in nonlinear functional analysis combining with qualitative analysis method in ordinary differential equations, we discuss the existence of nontrivial periodic orbits for higher dimensional autonomous system with small perturbations.

Role-based Bayesian decision framework for autonomous unmanned systems

In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.

The Challenges Posed by Autonomous Weapon Systems to Human Rights and Humanitarian Concerns and Relevant Legal Responses

By 2050,autonomous weapon systems may potentially replace humans as the main force on the battlefield,as per predictions.The development of autonomous weapon systems poses risks to human rights and humanitarian concerns and raises questions about how international law should regulate new technologies.From the perspectives of international human rights law and international humanitarian law,autonomous weapon systems present serious challenges in terms of invasiveness,indiscriminate killing,cruelty,and loss of control,which impact human rights and humanitarian principles.Against the backdrop of increased attention to the protection of human rights in China,it is necessary to clarify the existing regulatory framework and fundamental stance regarding autonomous weapon systems and proactively consider and propose countermeasures to address the risks associated with such systems.This will help prevent human rights and humanitarian violations and advance the timely resolution of this issue,which affects the future and destiny of humanity,ultimately achieving the noble goal of universal enjoyment of human rights.

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning

Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.

An Automated Approach for Software Fault Detection and Recovery

Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.

WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

Human feedback enhanced autonomous intelligent systems:a perspective from intelligent driving

Artificial intelligence empowers the rapid development of autonomous intelligent systems(AISs),but it still struggles to cope with open,complex,dynamic,and uncertain environments,limiting its large-scale industrial application.Reliable human feedback provides a mechanism for aligning machine behavior with human values and holds promise as a new paradigm for the evolution and enhancement of machine intelligence.This paper analyzes the engineering insights from ChatGPT and elaborates on the evolution from traditional feedback to human feedback.Then,a unified framework for self-evolving intelligent driving(ID)based on human feedback is proposed.Finally,an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.

Monocular depth estimation based on deep learning: An overview

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image(monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

Update Chain-based Approach for Checking Route Oscillation of BGP

This paper presents a novel approach for checking route oscillation of border gateway protocol（BGP） quickly,by which the privacy of routing policies of autonomous system（AS） can be respected.Firstly,route update chain tag（RUCT） is constructed to track the forwarding of update report,and local routing library is made to record the changing history of update report.Then route oscillation can be identified by analyzing correlative state of RUCT and local routing library.The characteristic of this approach is that an arbitrary AS can check route oscillation alone only by sharing its network ID,which greatly respects the pri-vacy of routing policies for each AS.This paper shows that both looping in RUCT and consecutive positive-negative report in local record are sufficient conditions for route oscillation.Comparative experiments demonstrate the availability and efficiency of the proposed approach.

A historical review of the development of verification and validation theories for simulation models

Modeling and simulation(M&S)play a critical role in both engineering and basic research processes.Computer-based models have existed since the 1950s,and those early models have given way to the more complex computational and physics-based simula-tions used today.As such,a great deal of research has been done to establish what level of trust should be given to simulation outputs and how to verify and validate the mod-els used in these simulations.This paper presents an overview of the theoretical work done to date defining formal definitions for,and methods of,verification and validation(V&V)of computer models.Simulation models are broken down into three broad cate-gories:analytical and simulation models,computational and physics-based models,and simulations of autonomous systems,and the unique theories and methods developed to address V&V of these models are presented.This paper also presents the current prob-lems in the theoretical field of V&V for models as simulations move from single system models and simulations to more complex simulation tools.In particular,this paper high-lights the lack of agreed-upon methods for V&V of simulations of autonomous systems,such as an autonomous unmanned vehicles,and proposes some next steps needed to address this problem.

Global stability results for models of commensalism

We analyze the global stability of the coexisting equilibria for several models of commensalism, first by devising a procedure to modify several Lyapunov functionals which were introduced earlier for corresponding models of mutualism, further confirming their usefulness. It is seen that commensalism promotes global stability, in connection with higher-order self-limiting terms which prevent unboundedness. We then use the theory of asymptotically autonomous systems to prove global stability results for models of commensalism which are subject to Allee effects, finding that commensalisms of appropriate strength can overcome the influence of strong Allee effects.

Quantum computing in intelligent transportation systems:A survey

Quantum computing,a field utilizing the principles of quantum mechanics,promises great advancements across various industries.This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems,exploring its potential to transform areas such as traf­fic optimization,logistics,routing,and autonomous vehicles.By examining current research efforts,challenges,and future directions,this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation.

A Rigorous Architectural Approach to Adaptive Software Engineering

The engineering of distributed adaptive software is a complex task which requires a rigorous approach. Software architectural （structural） concepts and principles are highly beneficial in specifying, designing, analysing, constructing and evolving distributed software. A rigorous architectural approach dictates formalisms and techniques that are compositional, components that are context independent and systems that can be constructed and evolved incrementally. This paper overviews some of the underlying reasons for adopting an architectural approach, including a brief ＂rational history＂ of our research work, and indicates how an architectural model can potentially facilitate the provision of self-managed adaptive software system.

On c-equivalence

In this paper,we propose some c-equivalence specifically for autonomous Lagrangian systems and show how to construct connecting orbits in energy surface based on this c-equivalence.