It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace an...It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.展开更多
针对仿人机器人的步态行走存在不稳定和目前倒立摆模型的预观控制存在ZMP(Zero Moment Poin)跟踪精度不足的问题,文中在倒立摆模型上结合预观控制理论,对算法进行改进,并采用机器人的七连杆模型的ZMP计算结果作为预观控制的参考输入,利...针对仿人机器人的步态行走存在不稳定和目前倒立摆模型的预观控制存在ZMP(Zero Moment Poin)跟踪精度不足的问题,文中在倒立摆模型上结合预观控制理论,对算法进行改进,并采用机器人的七连杆模型的ZMP计算结果作为预观控制的参考输入,利用实际的ZMP和目标的ZMP之间的误差量,对倒立摆模型进行补偿,规划机器人的质心轨迹,从而使倒立摆补偿ZMP预观控制模型拥有更好的ZMP跟踪效果。通过MATLAB和Adams机械动力学仿真软件进行联合仿真验证了:改进预观控制和ZMP补偿优化方法对倒立摆模型改进的有效性,并且提升了仿人机器人在行走过程中的稳定裕度。展开更多
针对自主车辆换道轨迹跟踪精度较低等问题进行了研究。提出了基于轨迹预测的多点预瞄权重增益分配的方法。首先,根据车辆与路径的实时横向偏差以及航向角偏差,建立驾驶员转向模型,获得最优方向盘转角;其次,为了提高车辆换道路径跟踪时...针对自主车辆换道轨迹跟踪精度较低等问题进行了研究。提出了基于轨迹预测的多点预瞄权重增益分配的方法。首先,根据车辆与路径的实时横向偏差以及航向角偏差,建立驾驶员转向模型,获得最优方向盘转角;其次,为了提高车辆换道路径跟踪时的稳定性,采用线性模型预测控制(linear model predictive control,L-MPC)策略设计轨迹跟踪控制器。最后,搭建Carsim&Simulink联合仿真模型,针对不同车速设置对比实验进行分析,结果表明基于轨迹预测的驾驶员模型能较好地跟踪换道轨迹,且稳态行驶下的路径跟踪最大横向误差为8.1%,但在高速极限工况时路径跟踪适应性较差,而L-MPC策略在高速时具有更好的路径跟踪精度及稳定性,其跟踪误差小于4%。展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 60875067the Natural Science Foundation of Heilongjiang Province under Grant F200602the Technical Innovation Talent Foundation of Harbin under Grant 2010RFQXG010
文摘It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.
文摘针对仿人机器人的步态行走存在不稳定和目前倒立摆模型的预观控制存在ZMP(Zero Moment Poin)跟踪精度不足的问题,文中在倒立摆模型上结合预观控制理论,对算法进行改进,并采用机器人的七连杆模型的ZMP计算结果作为预观控制的参考输入,利用实际的ZMP和目标的ZMP之间的误差量,对倒立摆模型进行补偿,规划机器人的质心轨迹,从而使倒立摆补偿ZMP预观控制模型拥有更好的ZMP跟踪效果。通过MATLAB和Adams机械动力学仿真软件进行联合仿真验证了:改进预观控制和ZMP补偿优化方法对倒立摆模型改进的有效性,并且提升了仿人机器人在行走过程中的稳定裕度。
文摘针对自主车辆换道轨迹跟踪精度较低等问题进行了研究。提出了基于轨迹预测的多点预瞄权重增益分配的方法。首先,根据车辆与路径的实时横向偏差以及航向角偏差,建立驾驶员转向模型,获得最优方向盘转角;其次,为了提高车辆换道路径跟踪时的稳定性,采用线性模型预测控制(linear model predictive control,L-MPC)策略设计轨迹跟踪控制器。最后,搭建Carsim&Simulink联合仿真模型,针对不同车速设置对比实验进行分析,结果表明基于轨迹预测的驾驶员模型能较好地跟踪换道轨迹,且稳态行驶下的路径跟踪最大横向误差为8.1%,但在高速极限工况时路径跟踪适应性较差,而L-MPC策略在高速时具有更好的路径跟踪精度及稳定性,其跟踪误差小于4%。
