The three dimensional ray-path equation considering the effect of the horizontal gradient of the ionospheric electron density is obtained. By integrating in upgoing and downgoing stage, the point on the raypath can be...The three dimensional ray-path equation considering the effect of the horizontal gradient of the ionospheric electron density is obtained. By integrating in upgoing and downgoing stage, the point on the raypath can be determined and the propagation channel in HF radio telecommunication may be recknoed and predicted. Considering the effect of the electron density slope the ray-path is asymmetric about the reflecting point, and the wave is no longer in the great circle determined by the original wave vector. The group path and the phase path can also be solved.展开更多
针对现有非结构化环境下的无人车辆全局路径规划的安全性和稳定性问题,提出了一种考虑车辆斜坡侧翻稳定性的EA-A^(*)算法(evaluation of adaptation A^(*),EA-A^(*))。建立车辆在斜坡时的动力学模型,获取车辆在斜坡安全行驶的极限参数;...针对现有非结构化环境下的无人车辆全局路径规划的安全性和稳定性问题,提出了一种考虑车辆斜坡侧翻稳定性的EA-A^(*)算法(evaluation of adaptation A^(*),EA-A^(*))。建立车辆在斜坡时的动力学模型,获取车辆在斜坡安全行驶的极限参数;基于数字高程模型(DEM)提取坡度模型、起伏度模型和粗糙度模型并整合成具有各个地形参数的适应度地图模型;增加车辆侧翻稳定性约束完善代价函数,并采用自适应分辨率EA-A^(*)规划算法得到优化路径。仿真表明:相比传统三维A^(*)算法,EA-A^(*)算法所规划的路径更为合理,危险道路显著减少,平均坡度由30.43°降低到7.78°;采用了自适应分辨率的规划方法后减少了96.27%的规划时间,更满足现实需求。展开更多
The continuous development of smart agriculture puts forward the requirement of high accuracy slope path tracking for the agricultural wheel-legged robot.Compared to flat terrain,path tracking control on sloped terrai...The continuous development of smart agriculture puts forward the requirement of high accuracy slope path tracking for the agricultural wheel-legged robot.Compared to flat terrain,path tracking control on sloped terrain faces the obstacle of motion instability of the wheel-legged robot induced by the slope gravitational force component,which causes instantaneous steering center to offset.To address this problem,this study proposed a slope path tracking control algorithm by combining the methods of virtual sensing radar and two-level neural network.Firstly,the kinematic and dynamic models of the wheel-legged robot are deduced,from which the crucial factors affecting control accuracy of slope path tracking are recognized.Secondly,this study constructs the slope path tracking control algorithm,in which the virtual sensing radar is utilized to realize route perception,and the two-level neural network is employed to provide drive motors’speeds to adapt to path tracking on different slopes.Furthermore,the corresponding compensation methods of the identified impacting factors are embedded in the proposed algorithm,including the lateral tracking deviation factor,heading angle deviation factor,slope change factor,and slip rate factor.Finally,the co-simulation model of slope path tracking control is constructed,including the multi-body dynamic model of the wheel-legged robot in RecurDyn and the proposed slope path tracking algorithm complied by Python.Subsequently,the simulation tests of the wheel-legged robot are carried out under various slope angles and velocities.The results reveal that the proposed algorithm’s effectiveness and accuracy are superior,with tracking errors reduced by more than 47.2%compared to an optimized pure pursuit algorithm.展开更多
文摘The three dimensional ray-path equation considering the effect of the horizontal gradient of the ionospheric electron density is obtained. By integrating in upgoing and downgoing stage, the point on the raypath can be determined and the propagation channel in HF radio telecommunication may be recknoed and predicted. Considering the effect of the electron density slope the ray-path is asymmetric about the reflecting point, and the wave is no longer in the great circle determined by the original wave vector. The group path and the phase path can also be solved.
文摘针对现有非结构化环境下的无人车辆全局路径规划的安全性和稳定性问题,提出了一种考虑车辆斜坡侧翻稳定性的EA-A^(*)算法(evaluation of adaptation A^(*),EA-A^(*))。建立车辆在斜坡时的动力学模型,获取车辆在斜坡安全行驶的极限参数;基于数字高程模型(DEM)提取坡度模型、起伏度模型和粗糙度模型并整合成具有各个地形参数的适应度地图模型;增加车辆侧翻稳定性约束完善代价函数,并采用自适应分辨率EA-A^(*)规划算法得到优化路径。仿真表明:相比传统三维A^(*)算法,EA-A^(*)算法所规划的路径更为合理,危险道路显著减少,平均坡度由30.43°降低到7.78°;采用了自适应分辨率的规划方法后减少了96.27%的规划时间,更满足现实需求。
基金supported by the National Key R&D Program of China(Grant No.2022YFD2202102)the Key Laboratory of Modern Agricultural Intelligent Equipment in South China,Ministry of Agriculture and Rural Affairs,China.
文摘The continuous development of smart agriculture puts forward the requirement of high accuracy slope path tracking for the agricultural wheel-legged robot.Compared to flat terrain,path tracking control on sloped terrain faces the obstacle of motion instability of the wheel-legged robot induced by the slope gravitational force component,which causes instantaneous steering center to offset.To address this problem,this study proposed a slope path tracking control algorithm by combining the methods of virtual sensing radar and two-level neural network.Firstly,the kinematic and dynamic models of the wheel-legged robot are deduced,from which the crucial factors affecting control accuracy of slope path tracking are recognized.Secondly,this study constructs the slope path tracking control algorithm,in which the virtual sensing radar is utilized to realize route perception,and the two-level neural network is employed to provide drive motors’speeds to adapt to path tracking on different slopes.Furthermore,the corresponding compensation methods of the identified impacting factors are embedded in the proposed algorithm,including the lateral tracking deviation factor,heading angle deviation factor,slope change factor,and slip rate factor.Finally,the co-simulation model of slope path tracking control is constructed,including the multi-body dynamic model of the wheel-legged robot in RecurDyn and the proposed slope path tracking algorithm complied by Python.Subsequently,the simulation tests of the wheel-legged robot are carried out under various slope angles and velocities.The results reveal that the proposed algorithm’s effectiveness and accuracy are superior,with tracking errors reduced by more than 47.2%compared to an optimized pure pursuit algorithm.
