目的:运用计算机辅助药物设计寻找川芎抗血栓活性成分。方法:以"血栓症"为关键词,从治疗靶蛋白数据库中搜索筛选川芎抗血栓靶蛋白;再从蛋白质数据库中查询目标靶蛋白并下载其三维结构,采用蛋白质预处理工具确定活性区域中心坐...目的:运用计算机辅助药物设计寻找川芎抗血栓活性成分。方法:以"血栓症"为关键词,从治疗靶蛋白数据库中搜索筛选川芎抗血栓靶蛋白;再从蛋白质数据库中查询目标靶蛋白并下载其三维结构,采用蛋白质预处理工具确定活性区域中心坐标,应用PyRx和Discovery Studio Visualizer软件对从台湾中医药资料库下载的247个川芎小分子与靶蛋白进行分子对接,通过结合能筛选出活性成分并分析结合作用力。结果:筛选出4个活性成分即新绿原酸、1-H-苯并咪唑-2-胺、3,8-二羟基酰内酯、川芎三萜,其分别与凝血酶、抗凝血酶Ⅲ、凝血因子Ⅹa、血栓调节蛋白具有较高结合活性,结合能分别为-6.1、-4.5、-7.7、-8.6 k J/mol;分析结果显示范德华力、静电作用力在对接中发挥着重要作用。结论:新绿原酸、1-H-苯并咪唑-2-胺、3,8-二羟基酰内酯、川芎三萜可能是中药川芎抗血栓的活性成分。展开更多
针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法(simplified obstacles and predict collision of artificial potential field meth...针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法(simplified obstacles and predict collision of artificial potential field method,SOPC-APF),算法引入预测碰撞思想,在机器人未进入陷阱区域或者极小点问题前做出决策;对于多障碍物的斥力与目标点的引力产生的合力使机器人陷入震荡,提出简化障碍物,即简化为影响范围内目标点一侧的受限障碍物;针对目标不可达问题,在碰撞预测基础上,设定虚拟目标点,经改进的斥力函数引导机器人快速生成一条平滑、平稳、无碰撞的路径。通过与传统算法、改进APF算法以及改进蚁群算法的仿真对比实验表明,SOPC-APF有效解决了人工势场法不适用于多障碍物复杂环境的问题,以及传统算法容易陷入陷阱区域和局部极小点问题。展开更多
The paper proposes a Virtual Target Guidance(VTG)-based distributed Model Predictive Control(MPC) scheme for formation control of multiple Unmanned Aerial Vehicles(UAVs).First, a framework of distributed MPC scheme is...The paper proposes a Virtual Target Guidance(VTG)-based distributed Model Predictive Control(MPC) scheme for formation control of multiple Unmanned Aerial Vehicles(UAVs).First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem(FHOCP) can be solved by swarm intelligent optimization algorithm.Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance.Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function.Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.展开更多
A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through ap...A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.展开更多
文摘目的:运用计算机辅助药物设计寻找川芎抗血栓活性成分。方法:以"血栓症"为关键词,从治疗靶蛋白数据库中搜索筛选川芎抗血栓靶蛋白;再从蛋白质数据库中查询目标靶蛋白并下载其三维结构,采用蛋白质预处理工具确定活性区域中心坐标,应用PyRx和Discovery Studio Visualizer软件对从台湾中医药资料库下载的247个川芎小分子与靶蛋白进行分子对接,通过结合能筛选出活性成分并分析结合作用力。结果:筛选出4个活性成分即新绿原酸、1-H-苯并咪唑-2-胺、3,8-二羟基酰内酯、川芎三萜,其分别与凝血酶、抗凝血酶Ⅲ、凝血因子Ⅹa、血栓调节蛋白具有较高结合活性,结合能分别为-6.1、-4.5、-7.7、-8.6 k J/mol;分析结果显示范德华力、静电作用力在对接中发挥着重要作用。结论:新绿原酸、1-H-苯并咪唑-2-胺、3,8-二羟基酰内酯、川芎三萜可能是中药川芎抗血栓的活性成分。
文摘针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法(simplified obstacles and predict collision of artificial potential field method,SOPC-APF),算法引入预测碰撞思想,在机器人未进入陷阱区域或者极小点问题前做出决策;对于多障碍物的斥力与目标点的引力产生的合力使机器人陷入震荡,提出简化障碍物,即简化为影响范围内目标点一侧的受限障碍物;针对目标不可达问题,在碰撞预测基础上,设定虚拟目标点,经改进的斥力函数引导机器人快速生成一条平滑、平稳、无碰撞的路径。通过与传统算法、改进APF算法以及改进蚁群算法的仿真对比实验表明,SOPC-APF有效解决了人工势场法不适用于多障碍物复杂环境的问题,以及传统算法容易陷入陷阱区域和局部极小点问题。
基金supported in part by the National Natural Science Foundation of China(No.61803009)Fundamental Research Funds for the Central Universities,China(No.YWF-19-BJ-J-205)Aeronautical Science Foundation of China(No.20175851032)。
文摘The paper proposes a Virtual Target Guidance(VTG)-based distributed Model Predictive Control(MPC) scheme for formation control of multiple Unmanned Aerial Vehicles(UAVs).First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem(FHOCP) can be solved by swarm intelligent optimization algorithm.Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance.Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function.Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.
文摘A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.
