Dear Editor,This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles(UGVs).A recent work[1]has demonstrated the effectiveness of approximating the optimal state ...Dear Editor,This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles(UGVs).A recent work[1]has demonstrated the effectiveness of approximating the optimal state feedback for a nonlinear unmanned system via deep neural network(DNN).展开更多
This paper presents a novel autonomous rescue system for bushfire surveillance and evacuation in mountainous terrains using collaborating Unmanned Aerial Vehicles(UAVs)and an Unmanned Ground Vehicle(UGV).The system in...This paper presents a novel autonomous rescue system for bushfire surveillance and evacuation in mountainous terrains using collaborating Unmanned Aerial Vehicles(UAVs)and an Unmanned Ground Vehicle(UGV).The system introduces(1)a 3D hierarchical hybrid navigation algorithm that integrates UAV coverage path planning with UGV reactive path planning,an adaptive communication framework ensuring continuous line-of-sight connectivity,and a multi-objective optimization model balancing rescue efficiency with system constraints.Simulations across three scenarios demonstrate the system's effectiveness,with the multi-vehicle configuration achieving 54%faster evacuation times(38.8 s vs 85.1 s)compared to single-vehicle systems while maintaining full coverage.Results validate the framework's capability to handle complex terrain features and communication constraints in autonomous bushfire monitoring and evacuation operations.展开更多
The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned gr...The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.展开更多
This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three...This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).展开更多
Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematic...Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.展开更多
This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dime...This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dimensional, nonlinear and non-Gaussian generalization of the classical Kalman servo system. After defining the Kalman servo as a motivation, we define the affine Hamiltonian neural network for adaptive nonlinear control of a team of UGVs in continuous time. We then define a high-dimensional Bayesian particle filter for estimation of a team of UGVs in discrete time. Finally, we formulate a hybrid Hamiltonian servo system by combining the continuous-time control and the discrete-time estimation into a coherent framework that works like a predictor-corrector system.展开更多
Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs)have been used in research and development community due to their strong potential in high-risk missions.One of the most important civilian implementatio...Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs)have been used in research and development community due to their strong potential in high-risk missions.One of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires,the focus of this paper.However,wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the mission.In this paper,wildfire growth is simulated using a coupled Partial Differential Equation(PDE)model,widely used in literature for modeling wildfires,in a grid environment with added process and measurement noise.Using principles of Proper Orthogonal Decomposition(POD),and with an appropriate choice of decomposition modes,a low-dimensional equivalent fire growth model is obtained for the deployment of the space-time Kalman Filtering(KF)paradigm for estimation of wildfires using simulated data.The KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the UAV.This information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on wildfire.Path planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk function.The results from numerical simulation are presented to validate the proposed estimation and path planning methods.展开更多
Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical,but also very challenging and risky.Despite first responders putting their lives at risk in saving others,huma...Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical,but also very challenging and risky.Despite first responders putting their lives at risk in saving others,human-physical limits cause delays in response time,resulting in fatality and property damage.In this paper,we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster.The framework consists of unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),a cloud-based remote control station(RCS).A light-weight message queuing telemetry transport(MQTT)based communication is adopted for facilitating collaboration between autonomous systems.To effectively work under unfavorable disaster conditions,antenna tracker is developed as a tool to extend network coverage to distant areas,and mobile charging points for the UAVs are also implemented.The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language(AADL).Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols,and the implemented UAV control mechanisms are functioning properly.Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.展开更多
Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and ...Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments.展开更多
文摘Dear Editor,This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles(UGVs).A recent work[1]has demonstrated the effectiveness of approximating the optimal state feedback for a nonlinear unmanned system via deep neural network(DNN).
基金supported by the Australian Research Councilfunding from the Australian Government,via grant AUSMURIB000001 associated with ONR MURI grant N00014-19-1-2571
文摘This paper presents a novel autonomous rescue system for bushfire surveillance and evacuation in mountainous terrains using collaborating Unmanned Aerial Vehicles(UAVs)and an Unmanned Ground Vehicle(UGV).The system introduces(1)a 3D hierarchical hybrid navigation algorithm that integrates UAV coverage path planning with UGV reactive path planning,an adaptive communication framework ensuring continuous line-of-sight connectivity,and a multi-objective optimization model balancing rescue efficiency with system constraints.Simulations across three scenarios demonstrate the system's effectiveness,with the multi-vehicle configuration achieving 54%faster evacuation times(38.8 s vs 85.1 s)compared to single-vehicle systems while maintaining full coverage.Results validate the framework's capability to handle complex terrain features and communication constraints in autonomous bushfire monitoring and evacuation operations.
基金supported by the National Natural Science Foundation of China(No.62171486 and No.U2001213)the Guangdong Basic and Applied Basic Research Project(2022A1515140166)。
文摘The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054,60975072)the National Basic Research Program of China("973" Project)(Grant No.2013CB035503)+3 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Top-Notch Young Talents Program of Chinathe Fundamental Research Funds for the Central Universities of Chinathe Aeronautical Foundation of China(Grant No.20115151019)
文摘This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).
基金supported by the National Natural Science Foundation of China (Grant Nos. 60975072 and 60604009)Aeronautical Science Foundation of China (Grant No. 2008ZC01006)+4 种基金Program for New Century Excellent Talents in University of China (Grant No. NCET-10-0021)the Fundamental Research Funds for the Central Universities of China (Grant No. YWF-10-01-A18)Beijing NOVA Program Foundation (Grant No. 2007A017)open Fund of the State Key Laboratory of Virtual Reality Technology and SystemsOpen Fund of the Provincial Key Laboratory for Information Processing Technology, Suzhou University, China (Grant No. KJS1020)
文摘Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.
文摘This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dimensional, nonlinear and non-Gaussian generalization of the classical Kalman servo system. After defining the Kalman servo as a motivation, we define the affine Hamiltonian neural network for adaptive nonlinear control of a team of UGVs in continuous time. We then define a high-dimensional Bayesian particle filter for estimation of a team of UGVs in discrete time. Finally, we formulate a hybrid Hamiltonian servo system by combining the continuous-time control and the discrete-time estimation into a coherent framework that works like a predictor-corrector system.
文摘Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs)have been used in research and development community due to their strong potential in high-risk missions.One of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires,the focus of this paper.However,wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the mission.In this paper,wildfire growth is simulated using a coupled Partial Differential Equation(PDE)model,widely used in literature for modeling wildfires,in a grid environment with added process and measurement noise.Using principles of Proper Orthogonal Decomposition(POD),and with an appropriate choice of decomposition modes,a low-dimensional equivalent fire growth model is obtained for the deployment of the space-time Kalman Filtering(KF)paradigm for estimation of wildfires using simulated data.The KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the UAV.This information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on wildfire.Path planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk function.The results from numerical simulation are presented to validate the proposed estimation and path planning methods.
基金supported partially by AirForce Research Laboratory,the Office of the Secretary of Defense(OSD)(FA8750-15-2-0116)the National Science Foundation(NSF)(1832110)the National Institute of Aerospace and Langley(C16-2B00-NCAT)。
文摘Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical,but also very challenging and risky.Despite first responders putting their lives at risk in saving others,human-physical limits cause delays in response time,resulting in fatality and property damage.In this paper,we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster.The framework consists of unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),a cloud-based remote control station(RCS).A light-weight message queuing telemetry transport(MQTT)based communication is adopted for facilitating collaboration between autonomous systems.To effectively work under unfavorable disaster conditions,antenna tracker is developed as a tool to extend network coverage to distant areas,and mobile charging points for the UAVs are also implemented.The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language(AADL).Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols,and the implemented UAV control mechanisms are functioning properly.Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.
文摘Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments.
