Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This stu...Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.展开更多
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 an...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.展开更多
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 H...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.展开更多
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 necessar...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.展开更多
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 ...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.展开更多
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...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.展开更多
The dawn of Autonomous Systems has marked a pivotal transformation in the realm of technology,establishing new paradigms in various industries,from healthcare to transportation.These systems,characterized by their abi...The dawn of Autonomous Systems has marked a pivotal transformation in the realm of technology,establishing new paradigms in various industries,from healthcare to transportation.These systems,characterized by their ability to operate without human intervention,promise enhanced efficiency,precision,and adaptability in tasks otherwise prone to human error or constraints.However,as with all revolutionary innovations,they bring forth a plethora of challenges.This paper aims to provide a comprehensive exploration of Autonomous Systems,discussing their architectures,applications,and the intricacies of their operation.We delve into the pressing challenges,both ethical and technical,and speculate on the potential future trajectories of this transformative technology.By amalgamating insights from diverse disciplines,this paper offers a holistic perspective on Autonomous Systems,setting the stage for informed discourse and future research endeavors.展开更多
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 unmanne...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.展开更多
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 concer...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.展开更多
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 interest...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%.展开更多
1 Introduction Autonomous driving technology has made significant advancements in recent years.The evolution of autonomous driving systems from traditional modular designs to end-to-end learning paradigms has led to c...1 Introduction Autonomous driving technology has made significant advancements in recent years.The evolution of autonomous driving systems from traditional modular designs to end-to-end learning paradigms has led to comprehensive improvements in driving capabilities.In modular designs,driving tasks are segmented into independent modules,such as perception,decision-making,planning,and control.展开更多
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 r...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.展开更多
Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of c...Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of crop management.This paper introduces an innovative approach to integrated cotton management,involving the implementation of an Autonomous Cycle of Data Analysis Tasks(ACODAT).The proposed autonomous cycle is composed of a classification task of the population of pests(boll weevil)(based on eXtreme Gradient Boosting-XGBoost),a diagnosis-prediction task of cotton yield(based on a fuzzy system),and a prescription task of strategies for the adequate management of the crop(based on genetic algorithms).The proposed system can evaluate several variables according to the conditions of the crop,and recommend the best strategy for increasing the cotton yield.In particular,the classification task has an accuracy of 88%,the diagnosis/prediction task obtained an accuracy of 98%,and the genetic algorithm recommends the best strategy for the context analyzed.Focused on integrated cotton management,our system offers flexibility and adaptability,which facilitates the incorporation of new tasks.展开更多
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 ap...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.展开更多
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...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.展开更多
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 ap...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.展开更多
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 pape...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.展开更多
Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing...Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing how goods are processed,stored,and transported.This paper explores the rapid evolution of warehouse automation,highlighting its role in improving efficiency,reducing costs,and reshaping industry standards.However,this technological shift is not without controversy.Labor unions and industry stakeholders continue to raise concerns about job displacement,economic restructuring,and the unintended consequences of large-scale automation.Does automation represent an existential threat to the workforce,or is it the key to a more resilient and optimized supply chain?By examining industry trends,case studies,and financial data,this study argues that resistance to automation signals its deep market penetration rather than a barrier to its adoption.As investments surge and technological integration accelerates,the debate surrounding automation is no longer about if it will dominate the industry but how businesses will adapt to its inevitable rise.Despite ongoing resistance,warehouse automation is becoming an irreversible cornerstone of modern logistics,pushing companies to redefine their operations or risk obsolescence in an increasingly automated world.展开更多
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...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.展开更多
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 fe...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.展开更多
基金partially funded by the Programa Nacional de Becas y Crédito Educativo of Peru and the Universitat de València,Spain.
文摘Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.
基金supported in part by the Department of National Defence’s Innovation for Defence Excellence and Security(IDEa S)Program,Canadathrough the Project of Auto Defence Towards Trustworthy Technologies for Autonomous Human-Machine Systems,NSERCthe IEEE SMC Society Technical Committee on Brain-Inspired Systems(TCBCS)。
文摘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.
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.10932002,11172120,and 11202090)
文摘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.
文摘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.
文摘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.
文摘The dawn of Autonomous Systems has marked a pivotal transformation in the realm of technology,establishing new paradigms in various industries,from healthcare to transportation.These systems,characterized by their ability to operate without human intervention,promise enhanced efficiency,precision,and adaptability in tasks otherwise prone to human error or constraints.However,as with all revolutionary innovations,they bring forth a plethora of challenges.This paper aims to provide a comprehensive exploration of Autonomous Systems,discussing their architectures,applications,and the intricacies of their operation.We delve into the pressing challenges,both ethical and technical,and speculate on the potential future trajectories of this transformative technology.By amalgamating insights from diverse disciplines,this paper offers a holistic perspective on Autonomous Systems,setting the stage for informed discourse and future research endeavors.
基金the Military Science Postgraduate Project of PLA(JY2020B006).
文摘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.
文摘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.
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa UniversityRiyadh,Saudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia.
文摘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%.
基金support of the Royal Society(Grant No.RG\R1\251434).
文摘1 Introduction Autonomous driving technology has made significant advancements in recent years.The evolution of autonomous driving systems from traditional modular designs to end-to-end learning paradigms has led to comprehensive improvements in driving capabilities.In modular designs,driving tasks are segmented into independent modules,such as perception,decision-making,planning,and control.
基金The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023016).
文摘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.
基金supported by the Colombian Science,Technology,and Innovation Fund(FCTeI)of the General Royalty System(SGR)Universidad EAFITand Universidad de Córdoba.
文摘Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of crop management.This paper introduces an innovative approach to integrated cotton management,involving the implementation of an Autonomous Cycle of Data Analysis Tasks(ACODAT).The proposed autonomous cycle is composed of a classification task of the population of pests(boll weevil)(based on eXtreme Gradient Boosting-XGBoost),a diagnosis-prediction task of cotton yield(based on a fuzzy system),and a prescription task of strategies for the adequate management of the crop(based on genetic algorithms).The proposed system can evaluate several variables according to the conditions of the crop,and recommend the best strategy for increasing the cotton yield.In particular,the classification task has an accuracy of 88%,the diagnosis/prediction task obtained an accuracy of 98%,and the genetic algorithm recommends the best strategy for the context analyzed.Focused on integrated cotton management,our system offers flexibility and adaptability,which facilitates the incorporation of new tasks.
基金supported by the National Natural Science Foundation of China under Grant No.62088101.
文摘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.
基金National Basic Research Program of China (2011CB707000)Foundation for Innovative Research Groups of the National Natural Science Foundation of China (60921001)
文摘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.
文摘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.
文摘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.
文摘Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing how goods are processed,stored,and transported.This paper explores the rapid evolution of warehouse automation,highlighting its role in improving efficiency,reducing costs,and reshaping industry standards.However,this technological shift is not without controversy.Labor unions and industry stakeholders continue to raise concerns about job displacement,economic restructuring,and the unintended consequences of large-scale automation.Does automation represent an existential threat to the workforce,or is it the key to a more resilient and optimized supply chain?By examining industry trends,case studies,and financial data,this study argues that resistance to automation signals its deep market penetration rather than a barrier to its adoption.As investments surge and technological integration accelerates,the debate surrounding automation is no longer about if it will dominate the industry but how businesses will adapt to its inevitable rise.Despite ongoing resistance,warehouse automation is becoming an irreversible cornerstone of modern logistics,pushing companies to redefine their operations or risk obsolescence in an increasingly automated world.
基金All authors are partially supported by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by the Knut and Alice Wallenberg Foundation.The first and second authors are additionally supported by the ELLIIT Network Organization for Information and Communication Technology,Swedenthe Swedish Foundation for Strategic Research SSF(Smart Systems Project RIT15-0097)+1 种基金The second author is also supported by a RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology,ChinaThe fifth and eighth authors are additionally supported by the Swedish Research Council.
文摘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.
基金supported by the National Key Research and Development Program of China (Grant No. 2018YFC0809302)the National Natural Science Foundation of China (Grant Nos. 61988101,61751305 and 61673176)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No.JKH012016011)the Programme of Introducing Talents of Discipline to Universities (the “111” Project)(Grant No. B17017)。
文摘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.
