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
To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The cen...To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.展开更多
As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive s...As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system. The cognitive supervisor model is a formalization of the recently Nobel-awarded research in brain science on mammalian and human path integration and navigation, performed by the hippocampus. This is formalized here as an adaptive Hamiltonian path integral, and efficiently simulated for implementation on robotic vehicles as a pair of coupled nonlinear Schr?dinger equations. The meta-cognitive supervisor model is a modal logic of actions and plans that hinges on a weak causality relation that specifies when atoms may change their values without specifying that they must change. This relatively simple logic is decidable yet sufficiently expressive to support the level of inference needed in our application. The atoms and action primitives of the logic framework also provide a straight-forward way of connecting the meta-cognitive supervisor with the cognitive supervisor, with other modules, and to the meta-cognitive supervisors of other robotic platforms in the swarm.展开更多
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 Key R&D Program of China(No.2016YFD0701504).
文摘To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.
文摘As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system. The cognitive supervisor model is a formalization of the recently Nobel-awarded research in brain science on mammalian and human path integration and navigation, performed by the hippocampus. This is formalized here as an adaptive Hamiltonian path integral, and efficiently simulated for implementation on robotic vehicles as a pair of coupled nonlinear Schr?dinger equations. The meta-cognitive supervisor model is a modal logic of actions and plans that hinges on a weak causality relation that specifies when atoms may change their values without specifying that they must change. This relatively simple logic is decidable yet sufficiently expressive to support the level of inference needed in our application. The atoms and action primitives of the logic framework also provide a straight-forward way of connecting the meta-cognitive supervisor with the cognitive supervisor, with other modules, and to the meta-cognitive supervisors of other robotic platforms in the swarm.
