A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.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
Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate th...Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate the use of the technology into local or regional public safety and homeland security incidents. The purpose of this presentation is to explain several key factors to consider when using micro technologies and unmanned systems in support of public safety and homeland security officials. Real time information is critical to the decision making process for public safety and homeland security officials to make assessments and quickly resolve crisis situations. Unmanned micro-vehicles and micro technologies are well suited to remotely observe, gather essential information, and immediately relay it to incident responders. These technologies can provide extremely important support during responses to hostage situations, hazardous environments, search and rescue, natural disasters, border patrol and many others. The true benefit is having remote resources providing real time support to incident responders. This paper discusses the use of several different types of micro-vehicle platforms in public safety scenarios and their use of associated technologies such as GPS （Global Positioning System） autopilot, communication, and sensor devices.展开更多
With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliabil...With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliability and stability,and better serve human society.This article conducts adaptive cooperative secure tracking consensus of networked multiple unmanned systems subjected to false data injection attacks.From a practical perspective,each unmanned system is modeled using high-order unknown nonlinear discrete-time systems.To reduce the communication bandwidth between agents,a quantizer-based codec mechanism is constructed.This quantizer uses a uniform logarithmic quantizer,combining the advantages of both quantizers.Because the transmission information attached to the false data can affect the accuracy of the decoder,a new adaptive law is added to the decoder to overcome this difficulty.A distributed controller is devised in the backstepping framework.Rigorous mathematical analysis shows that our proposed control algorithms ensure that all signals of the resultant systems remain bounded.Finally,simulation examples reveal the practical utility of the theoretical analysis.展开更多
In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the t...In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.展开更多
Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increas...Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increase in the deployment of unmanned systems across a wide range of civilian and military applications,including unmanned aerial vehicles(UAVs),autonomous ground vehicles(AGVs),and unmanned surface vessels(USVs).It is critically important that effective analysis and control be carried out for these systems,especially when operating in complex and dynamic environments.The autonomous control capability has emerged as one of the key factors determining their success in task execution.Consequently,significant research efforts are now focused on enhancing the autonomy,robustness,and safety of unmanned systems,as well as exploring novel control strategies and advanced technical approaches to address these challenges.展开更多
Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of gro...Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence(AI).However,the current networked unmanned systems are primarily designed for and applicable to a narrow range of domain-specific missions,and do not have sufficient human-level intel-ligence and human needs fulfillment for the challenging missions in our lives.We propose in this paper a vision of human-centric networked unmanned systems:Unmanned Intelligent Cluster(UnIC).Within this vision,distributed unmanned systems and humans are connected via knowledge sharing and social awareness to achieve collaborative cognition.This paper details UnIC’s concept,sources of intelligence,and layered architecture,and reviews enabling technologies for achieving this vision.In addition to the technological aspects,the social acceptance issues are highlighted.展开更多
Many mechanical parts of multi-rotor unmanned aerial vehicle(MUAV)can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV.For multi-MUAV attitude systems that experienc...Many mechanical parts of multi-rotor unmanned aerial vehicle(MUAV)can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV.For multi-MUAV attitude systems that experience output dead-zone,external disturbance and actuator fault,a leader-following consensus anti-disturbance and fault-tolerant control(FTC)scheme is proposed in this paper.In the design process,the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks(NNs).In order to balance out the effects of external disturbance and actuator fault,a disturbance observer is designed to compensate for the aforementioned negative impacts.The Nussbaum function is used to address the problem of output dead-zone.The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique.Finally,the effectiveness of the control scheme is verified by simulation experiments.展开更多
System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose sign...System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.展开更多
With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not onl...With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.展开更多
Urban air mobility(UAM)is an emerging concept proposed in recent years that uses electric vertical takeoff and landing vehicles(eVTOLs).UAM is expected to offer an alternative way of transporting passengers and goods ...Urban air mobility(UAM)is an emerging concept proposed in recent years that uses electric vertical takeoff and landing vehicles(eVTOLs).UAM is expected to offer an alternative way of transporting passengers and goods in urban areas with significantly improved mobility by making use of low-altitude airspace.In addition to other essential elements,ground infrastructure of vertiports is needed to transition UAM from concept to operation.This study examines the network design of UAM on-demand service,with a particular focus on the use of integer programming and a solution algorithm to determine the optimal locations of vertiports,user allocation to vertiports,and vertiport access-and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand.A case study based on simulated disaggregate travel demand data of the Tampa Bay area in Florida,USA was conducted to demonstrate the effectiveness of the proposed model.Candidate vertiport locations were obtained by analyzing a three-dimensional(3D)geographic information system(GIS)map developed from lidar data of Florida and physical and regulation constraints of eVTOL operations at vertiports.Optimal locations of vertiports were determined to achieve the minimal total generalized cost;however,the modeling structure allows each user to select a better mode between ground transportation and UAM in terms of generalized cost.The outcomes of the case study reveal that although the percentage of trips that switched from ground mode to multimodal UAM was small,users choosing the UAM service benefited from significant time saving.In addition,the impact of different parameter settings on the demand for UAM service was explored from the supply side,and different pricing strategies were tested that might influence potential demand and revenue generation for UAM operators.The combined effects of the number of vertiports and pricing strategies were also analyzed.The findings from this study offer in-depth planning and managerial insights for municipal decision-makers and UAM operators.The conclusion of this paper discusses caveats to the study,ongoing efforts by the authors,and future directions in UAM research.展开更多
Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificia...Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.展开更多
The intersection of Quantum Technologies and Robotics Autonomy is explored in the present paper.The two areas are brought together in establishing an interdisciplinary interface that contributes to advancing the field...The intersection of Quantum Technologies and Robotics Autonomy is explored in the present paper.The two areas are brought together in establishing an interdisciplinary interface that contributes to advancing the field of system autonomy,and pushes the engineering boundaries beyond the existing techniques.The present research adopts the experimental aspects of quantum entanglement and quantum cryptography,and integrates these established quantum capabilities into distributed robotic platforms,to explore the possibility of achieving increased autonomy for the control of multi-agent robotic systems engaged in cooperative tasks.Experimental quantum capabilities are realized by producing single photons(using spontaneous parametric down-conversion process),polarization of photons,detecting vertical and horizontal polarizations,and single photon detecting/counting.Specifically,such quantum aspects are implemented on network of classical agents,i.e.,classical aerial and ground robots/unmanned systems.With respect to classical systems for robotic applications,leveraging quantum technology is expected to lead to guaranteed security,very fast control and communication,and unparalleled quantum capabilities such as entanglement and quantum superposition that will enable novel applications.展开更多
We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inte...We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered environments.The proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision avoidance.More specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free trajectory.Besides,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion planning.Finally,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.展开更多
Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent...Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.展开更多
This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images ...This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.展开更多
Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solve...Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.展开更多
There is a growing body of research indicating that drones can disturb animals.However,it is usu-ally unclear whether the disturbance is due to visual or auditory cues.Here,we examined the effect of drone flights on t...There is a growing body of research indicating that drones can disturb animals.However,it is usu-ally unclear whether the disturbance is due to visual or auditory cues.Here,we examined the effect of drone flights on the behavior of great dusky swifts Cypseloides senex and white collared swifts Streptoprocne zonaris in 2 breeding sites where drone noise was obscured by environmental noise from waterfalls and any disturbance must be largely visual.We performed 12 experimental flights with a multirotor drone at different vertical,horizontal,and diagonal distances from the colonies.From all flights,17%caused<1%of birds to temporarily a bandon the breeding site,50%caused half to abandon,and 33%caused more than half to abandon.We found that the diagonal distance explained 98.9%of the variability of the disturbance percentage and while at distances>50 m the disturbance percentage does not exceed 20%,at<40 m the disturbance percentage increase to>60%.We recommend that flights with a multirotor drone during the breeding period should be con-ducted at a distance of>50 m and that recreational flights should be discouraged or conducted at larger distances(e.g.100 m)in nesting birds areas such as waterfalls,canyons,and caves.展开更多
As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and d...As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and duration of small unmanned aerial vehicles. A simulation model of an X-models XCalubur electric motor-glider is used in combination with a refined 4D parametric thermal model to simulate soaring flight. The parametric thermal model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation.展开更多
Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployabl...Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployable small unmanned aerial systems(s UAS)in conjunction with powerful deep learning(DL)based object detection models are expected to play an important role for this application.To prove overall feasibility of this approach,this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield.Such a system is envisioned to involve an s UAS equipped with a modern electro-optical(EO)sensor and relying on a trained convolutional neural network(CNN).Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size,changes in aspect ratio and image scale,motion blur,occlusion,and camouflage.This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories.This study used a YOLOv2CNN(Res Net-50 backbone network)to train the model with ground truth data and demonstrated a high mean average precision(m AP)of 0.97 to detect and identify not only small pistols but also partially occluded rifles.展开更多
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
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Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate the use of the technology into local or regional public safety and homeland security incidents. The purpose of this presentation is to explain several key factors to consider when using micro technologies and unmanned systems in support of public safety and homeland security officials. Real time information is critical to the decision making process for public safety and homeland security officials to make assessments and quickly resolve crisis situations. Unmanned micro-vehicles and micro technologies are well suited to remotely observe, gather essential information, and immediately relay it to incident responders. These technologies can provide extremely important support during responses to hostage situations, hazardous environments, search and rescue, natural disasters, border patrol and many others. The true benefit is having remote resources providing real time support to incident responders. This paper discusses the use of several different types of micro-vehicle platforms in public safety scenarios and their use of associated technologies such as GPS （Global Positioning System） autopilot, communication, and sensor devices.
基金supported in part by the National Natural Science Foundation of China under Grant U20B2073,Grant 62103047Beijing Institute of Technology Research Fund Program for Young ScholarsYoung Elite Scientists Sponsorship Program by BAST(Grant No.BYESS2023365)
文摘With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliability and stability,and better serve human society.This article conducts adaptive cooperative secure tracking consensus of networked multiple unmanned systems subjected to false data injection attacks.From a practical perspective,each unmanned system is modeled using high-order unknown nonlinear discrete-time systems.To reduce the communication bandwidth between agents,a quantizer-based codec mechanism is constructed.This quantizer uses a uniform logarithmic quantizer,combining the advantages of both quantizers.Because the transmission information attached to the false data can affect the accuracy of the decoder,a new adaptive law is added to the decoder to overcome this difficulty.A distributed controller is devised in the backstepping framework.Rigorous mathematical analysis shows that our proposed control algorithms ensure that all signals of the resultant systems remain bounded.Finally,simulation examples reveal the practical utility of the theoretical analysis.
文摘In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.
文摘Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increase in the deployment of unmanned systems across a wide range of civilian and military applications,including unmanned aerial vehicles(UAVs),autonomous ground vehicles(AGVs),and unmanned surface vessels(USVs).It is critically important that effective analysis and control be carried out for these systems,especially when operating in complex and dynamic environments.The autonomous control capability has emerged as one of the key factors determining their success in task execution.Consequently,significant research efforts are now focused on enhancing the autonomy,robustness,and safety of unmanned systems,as well as exploring novel control strategies and advanced technical approaches to address these challenges.
基金supported by the National Natural Science Foundation of China (U1913602)the National Key Research and Development Program of China (2021YFF0601304)the Civilian Aircraft Research (MJG5-1N21)
文摘Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence(AI).However,the current networked unmanned systems are primarily designed for and applicable to a narrow range of domain-specific missions,and do not have sufficient human-level intel-ligence and human needs fulfillment for the challenging missions in our lives.We propose in this paper a vision of human-centric networked unmanned systems:Unmanned Intelligent Cluster(UnIC).Within this vision,distributed unmanned systems and humans are connected via knowledge sharing and social awareness to achieve collaborative cognition.This paper details UnIC’s concept,sources of intelligence,and layered architecture,and reviews enabling technologies for achieving this vision.In addition to the technological aspects,the social acceptance issues are highlighted.
基金supported by the National Natural Science Foundation of China(62033003,62003098)the Local Innovative and Research Teams Project of Guangdong Special Support Program(2019BT02X353)the China Postdoctoral Science Foundation(2019M662813,2020T130124,2020M682614).
文摘Many mechanical parts of multi-rotor unmanned aerial vehicle(MUAV)can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV.For multi-MUAV attitude systems that experience output dead-zone,external disturbance and actuator fault,a leader-following consensus anti-disturbance and fault-tolerant control(FTC)scheme is proposed in this paper.In the design process,the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks(NNs).In order to balance out the effects of external disturbance and actuator fault,a disturbance observer is designed to compensate for the aforementioned negative impacts.The Nussbaum function is used to address the problem of output dead-zone.The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique.Finally,the effectiveness of the control scheme is verified by simulation experiments.
文摘System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.
文摘With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.
文摘Urban air mobility(UAM)is an emerging concept proposed in recent years that uses electric vertical takeoff and landing vehicles(eVTOLs).UAM is expected to offer an alternative way of transporting passengers and goods in urban areas with significantly improved mobility by making use of low-altitude airspace.In addition to other essential elements,ground infrastructure of vertiports is needed to transition UAM from concept to operation.This study examines the network design of UAM on-demand service,with a particular focus on the use of integer programming and a solution algorithm to determine the optimal locations of vertiports,user allocation to vertiports,and vertiport access-and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand.A case study based on simulated disaggregate travel demand data of the Tampa Bay area in Florida,USA was conducted to demonstrate the effectiveness of the proposed model.Candidate vertiport locations were obtained by analyzing a three-dimensional(3D)geographic information system(GIS)map developed from lidar data of Florida and physical and regulation constraints of eVTOL operations at vertiports.Optimal locations of vertiports were determined to achieve the minimal total generalized cost;however,the modeling structure allows each user to select a better mode between ground transportation and UAM in terms of generalized cost.The outcomes of the case study reveal that although the percentage of trips that switched from ground mode to multimodal UAM was small,users choosing the UAM service benefited from significant time saving.In addition,the impact of different parameter settings on the demand for UAM service was explored from the supply side,and different pricing strategies were tested that might influence potential demand and revenue generation for UAM operators.The combined effects of the number of vertiports and pricing strategies were also analyzed.The findings from this study offer in-depth planning and managerial insights for municipal decision-makers and UAM operators.The conclusion of this paper discusses caveats to the study,ongoing efforts by the authors,and future directions in UAM research.
基金the United States Air Force Office of Scientific Research(AFOSR)contract FA9550-22-1-0268 awarded to KHA,https://www.afrl.af.mil/AFOSR/.The contract is entitled:“Investigating Improving Safety of Autonomous Exploring Intelligent Agents with Human-in-the-Loop Reinforcement Learning,”and in part by Jackson State University.
文摘Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.
文摘The intersection of Quantum Technologies and Robotics Autonomy is explored in the present paper.The two areas are brought together in establishing an interdisciplinary interface that contributes to advancing the field of system autonomy,and pushes the engineering boundaries beyond the existing techniques.The present research adopts the experimental aspects of quantum entanglement and quantum cryptography,and integrates these established quantum capabilities into distributed robotic platforms,to explore the possibility of achieving increased autonomy for the control of multi-agent robotic systems engaged in cooperative tasks.Experimental quantum capabilities are realized by producing single photons(using spontaneous parametric down-conversion process),polarization of photons,detecting vertical and horizontal polarizations,and single photon detecting/counting.Specifically,such quantum aspects are implemented on network of classical agents,i.e.,classical aerial and ground robots/unmanned systems.With respect to classical systems for robotic applications,leveraging quantum technology is expected to lead to guaranteed security,very fast control and communication,and unparalleled quantum capabilities such as entanglement and quantum superposition that will enable novel applications.
基金the Research Grants Council of Hong Kong SAR(Grant No:14206821 and Grant No:14217922)the Hong Kong Centre for Logistics Robotics(HKCLR).
文摘We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered environments.The proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision avoidance.More specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free trajectory.Besides,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion planning.Finally,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.
基金the Office of Naval Research for supporting this effort through the Consortium for Robotics and Unmanned Systems Education and Research。
文摘Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.
基金funded by the Center for Unmanned Aircraft Systems(C-UAS)a National Science Foundation Industry/University Cooperative Research Center(I/UCRC)under NSF award Numbers IIP-1161036 and CNS-1650547along with significant contributions from C-UAS industry members。
文摘This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.
基金supported by the National Natural Science Foundation of China under Grant No.62072475.
文摘Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.
文摘There is a growing body of research indicating that drones can disturb animals.However,it is usu-ally unclear whether the disturbance is due to visual or auditory cues.Here,we examined the effect of drone flights on the behavior of great dusky swifts Cypseloides senex and white collared swifts Streptoprocne zonaris in 2 breeding sites where drone noise was obscured by environmental noise from waterfalls and any disturbance must be largely visual.We performed 12 experimental flights with a multirotor drone at different vertical,horizontal,and diagonal distances from the colonies.From all flights,17%caused<1%of birds to temporarily a bandon the breeding site,50%caused half to abandon,and 33%caused more than half to abandon.We found that the diagonal distance explained 98.9%of the variability of the disturbance percentage and while at distances>50 m the disturbance percentage does not exceed 20%,at<40 m the disturbance percentage increase to>60%.We recommend that flights with a multirotor drone during the breeding period should be con-ducted at a distance of>50 m and that recreational flights should be discouraged or conducted at larger distances(e.g.100 m)in nesting birds areas such as waterfalls,canyons,and caves.
文摘As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and duration of small unmanned aerial vehicles. A simulation model of an X-models XCalubur electric motor-glider is used in combination with a refined 4D parametric thermal model to simulate soaring flight. The parametric thermal model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation.
文摘Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployable small unmanned aerial systems(s UAS)in conjunction with powerful deep learning(DL)based object detection models are expected to play an important role for this application.To prove overall feasibility of this approach,this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield.Such a system is envisioned to involve an s UAS equipped with a modern electro-optical(EO)sensor and relying on a trained convolutional neural network(CNN).Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size,changes in aspect ratio and image scale,motion blur,occlusion,and camouflage.This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories.This study used a YOLOv2CNN(Res Net-50 backbone network)to train the model with ground truth data and demonstrated a high mean average precision(m AP)of 0.97 to detect and identify not only small pistols but also partially occluded rifles.
