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
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
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
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
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
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.
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).
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 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(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.
