This paper presents a method of multicopter intercep-tion control based on visual servo and virtual tube in a cluttered environment.The proposed hybrid heuristic function improves the efficiency of the A*algorithm.The...This paper presents a method of multicopter intercep-tion control based on visual servo and virtual tube in a cluttered environment.The proposed hybrid heuristic function improves the efficiency of the A*algorithm.The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A*algorithm.In six dif-ferent simulation scenarios,the efficiency of the modified A*algorithm is 6.2%higher than that of the traditional A*algorithm.The efficiency of path planning and virtual tube planning is veri-fied by simulations.The effectiveness of interception control is verified by a software-in-loop(SIL)simulation.展开更多
针对在GPS信号弱/拒止和环境感知欠缺的环境下可重构海洋浮体的协同控制问题,本文提出了一种基于定相对位姿(Determined relative pose,DRP)视觉伺服模型的鲁棒非线性模型预测控制(Nonlinear model predictive control,NMPC)方案。可重...针对在GPS信号弱/拒止和环境感知欠缺的环境下可重构海洋浮体的协同控制问题,本文提出了一种基于定相对位姿(Determined relative pose,DRP)视觉伺服模型的鲁棒非线性模型预测控制(Nonlinear model predictive control,NMPC)方案。可重构海洋浮体的视觉伺服问题难点主要包括环境干扰强、系统非线性程度高、视觉伺服易陷入局部极值和可见性约束强。为应对这些难题,该视觉伺服控制策略需要实现:被控船仅依靠视觉信息进行多船协同控制;视觉伺服模型收敛性好;控制器具有一定鲁棒性且处理非线性系统和约束条件的能力强。为此,本研究首先建立了单浮体的动力学模型;然后将视觉模型、被控船艏摇信息及相机云台转角信息整合到系统状态中,形成了DRP模型,从而保证了双浮体视觉伺服控制结束后相对位姿的唯一性;接着结合浮体动力学模型和DRP模型,建立了基于图像的视觉伺服(Image based visual servo,IBVS)的系统模型,并对该系统模型进行分析,进而据此设计了鲁棒的NMPC控制器,以保证视觉伺服任务可以在强外界干扰的环境下进行;最后通过大量数值仿真实验验证了该方案的有效性。这些实验结果不仅证明了控制策略的稳定性和准确性,还展示了其在复杂环境下的鲁棒性能。展开更多
基金supported by the National Natural Science Foundation of China(62303350).
文摘This paper presents a method of multicopter intercep-tion control based on visual servo and virtual tube in a cluttered environment.The proposed hybrid heuristic function improves the efficiency of the A*algorithm.The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A*algorithm.In six dif-ferent simulation scenarios,the efficiency of the modified A*algorithm is 6.2%higher than that of the traditional A*algorithm.The efficiency of path planning and virtual tube planning is veri-fied by simulations.The effectiveness of interception control is verified by a software-in-loop(SIL)simulation.
基金Supported by National Natural Science Foundation of China（60874002） Key Project of Shanghai Education Committee （09ZZ158） Leading Academic Discipline Project of Shanghai Municipal Government （S30501）
基金Supported by Natural Science Foundation of Tianjin （07JCY- BJC05400）, Opening Project of National Laboratory of Industrial Control Technology of Zhejiang University （0708001）, Program for New Century Excellent Talents in University （NCET-06-0210）
文摘针对在GPS信号弱/拒止和环境感知欠缺的环境下可重构海洋浮体的协同控制问题,本文提出了一种基于定相对位姿(Determined relative pose,DRP)视觉伺服模型的鲁棒非线性模型预测控制(Nonlinear model predictive control,NMPC)方案。可重构海洋浮体的视觉伺服问题难点主要包括环境干扰强、系统非线性程度高、视觉伺服易陷入局部极值和可见性约束强。为应对这些难题,该视觉伺服控制策略需要实现:被控船仅依靠视觉信息进行多船协同控制;视觉伺服模型收敛性好;控制器具有一定鲁棒性且处理非线性系统和约束条件的能力强。为此,本研究首先建立了单浮体的动力学模型;然后将视觉模型、被控船艏摇信息及相机云台转角信息整合到系统状态中,形成了DRP模型,从而保证了双浮体视觉伺服控制结束后相对位姿的唯一性;接着结合浮体动力学模型和DRP模型,建立了基于图像的视觉伺服(Image based visual servo,IBVS)的系统模型,并对该系统模型进行分析,进而据此设计了鲁棒的NMPC控制器,以保证视觉伺服任务可以在强外界干扰的环境下进行;最后通过大量数值仿真实验验证了该方案的有效性。这些实验结果不仅证明了控制策略的稳定性和准确性,还展示了其在复杂环境下的鲁棒性能。
