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
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
We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reco...We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.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
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
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
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
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
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
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
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
Purpose–The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems(UASs).In addition,the aim of using two PID con...Purpose–The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems(UASs).In addition,the aim of using two PID controller is to achieve the position control and velocity control simultaneously.Design/methodology/approach–The dynamic of the UASs is mathematically modeled.One PID controller is used for position tracking control,while the other is selected for the vertical component of velocity tracking control.Meanwhile,fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.Findings–Based on this fuzzy logic algorithm,the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero.The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.Social implications–The acquired results are based on simulation not experiment.Originality/value–This is the first study to use two independent PID controllers to realize stable hovering control for UAS.It is also the first to use the velocity of the UAS to calculate the desired position.展开更多
In this paper,a nonlinear°ight control law is designed for a hybrid unmanned aerial vehicle(UAV)to achieve the advanced°ight performances with the autonomous mission management(AMM).The hybrid UAV is capable...In this paper,a nonlinear°ight control law is designed for a hybrid unmanned aerial vehicle(UAV)to achieve the advanced°ight performances with the autonomous mission management(AMM).The hybrid UAV is capable of hovering like quadrotors and maneuvering as-xed-wing aircraft.The main idea is to design the°ight control laws in modules.Those modules are organized online by the autonomous mission management.Such online organization will improve the UAV autonomy.One of the challenges is to execute the transition°ight between the rotary-wing and-xed-wing modes.The resulting closed-loop system with the designed°ight control law is veri-ed in simulation and the simulation results demonstrate that the resulting closed-loop system can successfully complete the designated°ight missions including the transition°ight between the rotary-wing and-xed-wing modes.展开更多
This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average(ARIMA)agent state prediction mechanism a...This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average(ARIMA)agent state prediction mechanism against periodic denial-of-service(Do S)attacks.The state predictor is used to predict the state of neighbor agents during periodic Do S attacks and maintain consistent control of multiple unmanned systems under Do S attacks.Considering the existing prediction error between the actual state and the predicted state,the estimated error is regarded as the uncertainty system disturbance,which is dealt with by the designed disturbance observer.The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term.Furthermore,this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic Do S attacks and reduce the frequency of actuator output changes.It is proved that the Zeno behavior can be excluded.Finally,the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.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
The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs pe...The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.展开更多
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.
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.
基金funding from the Australian Government,via grant AUSMURIB000001 associated with ONR MURI Grant N00014-19-1-2571。
文摘We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.
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.
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.
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.
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.
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.
文摘Purpose–The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems(UASs).In addition,the aim of using two PID controller is to achieve the position control and velocity control simultaneously.Design/methodology/approach–The dynamic of the UASs is mathematically modeled.One PID controller is used for position tracking control,while the other is selected for the vertical component of velocity tracking control.Meanwhile,fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.Findings–Based on this fuzzy logic algorithm,the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero.The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.Social implications–The acquired results are based on simulation not experiment.Originality/value–This is the first study to use two independent PID controllers to realize stable hovering control for UAS.It is also the first to use the velocity of the UAS to calculate the desired position.
文摘In this paper,a nonlinear°ight control law is designed for a hybrid unmanned aerial vehicle(UAV)to achieve the advanced°ight performances with the autonomous mission management(AMM).The hybrid UAV is capable of hovering like quadrotors and maneuvering as-xed-wing aircraft.The main idea is to design the°ight control laws in modules.Those modules are organized online by the autonomous mission management.Such online organization will improve the UAV autonomy.One of the challenges is to execute the transition°ight between the rotary-wing and-xed-wing modes.The resulting closed-loop system with the designed°ight control law is veri-ed in simulation and the simulation results demonstrate that the resulting closed-loop system can successfully complete the designated°ight missions including the transition°ight between the rotary-wing and-xed-wing modes.
基金supported by the National Natural Science Foundation of China(Nos.61833013,62003162,62233009)Natural Science Foundation of Jiangsu Province of China(Nos.BK20200416,BK20222012)+5 种基金China Postdoctoral Science Foundation(Nos.2020TQ0151,2020M681590)Fundamental Research Funds for the Central Universities(No.NS2021025)Industry-University Research Innovation Foundation for the Chinese Ministry of Education(No.2021ZYA02005)Science and Technology on Space Intelligent Control Laboratory(No.HTKJ2022KL502015)Aeronautical Science Foundation of China(No.20200007018001)Natural Sciences and Engineering Research Council of Canada
文摘This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average(ARIMA)agent state prediction mechanism against periodic denial-of-service(Do S)attacks.The state predictor is used to predict the state of neighbor agents during periodic Do S attacks and maintain consistent control of multiple unmanned systems under Do S attacks.Considering the existing prediction error between the actual state and the predicted state,the estimated error is regarded as the uncertainty system disturbance,which is dealt with by the designed disturbance observer.The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term.Furthermore,this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic Do S attacks and reduce the frequency of actuator output changes.It is proved that the Zeno behavior can be excluded.Finally,the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.
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.
基金by Cooperative Agreement AP20PPQS&T00C046 from the United States Department of Agriculture's Animal and Plant Health Inspection Service(APHIS).
文摘The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.