自由落猫机器人时间最优下落轨迹研究

Research on Time-Optimal Falling Trajectory of a Free-Falling Cat Robot

机器人在未知复杂环境下作业时,有从高处跌落的风险,所以需要考虑其空中的姿态调整能力,减轻由错误的着陆方式造成的伤害.当猫从高空坠落时,它总能通过自身调整安全落地.受这一生物学现象启发,通过研究猫能够安全落地的第一阶段,姿态调整阶段,首次提出以时间最优方式研究落猫机器人最优下落轨迹问题.首先利用轴对称双刚体模型导出机器人的数学模型,因为角速度不可积,将落猫机器人的姿态控制问题转变成非完整系统的运动规划问题.考虑到姿态调整所消耗的时间是决定调整结果较为重要的因素,随后以虚拟力矩输入取代真实角速度输入,建立时间优化函数,并构造了求解该函数的方法.然后以姿态调整耗时最短为控制目标,通过粒子群优化算法寻找目标最优解.最后运用所得最优解数据,在虚拟物理环境下进行了虚拟样机实验,并实现了落猫机器人的空中翻正动作.结果表明,该方法能有效地缩短自由下落机器人的姿态调整时间.

A robot may be at the risk of falling from a high place when it works in an unknown complex environment, so the attitude control ability of the robot in the air should be considered to reduce the damage caused by wrong landing attitudes. When a cat drops from a high space, it can always right itself and land safely. Inspired by this biological phenomenon, the optimal falling trajectory of a free-falling cat robot is studied in a time-optimal manner for the first time, through investigating the first stage of safe landing of a cat, namely the attitude adjustment stage. Firstly, a mathematical model of the robot is formulated based on an axisymmetric dual rigid-body model. Owing to the non-integrable angular velocity, the attitude control problem of the falling cat robot is transformed into a nonholonomic motion planning problem. Considering that the time consumption of attitude adjustment is an important factor to determine the adjustment result, a time-optimal function is built with the virtual torque input instead of the real angular velocity input, and a method to solve this function is set up also. Then, a particle swarm optimization algorithm is proposed to obtain the solution of objective function with the least time consumption of attitude adjustment. Finally, using the optimal solution data, the virtual prototype experiment is carried out in the virtual physical environment, and the righting movement in the air for a falling cat robot is realized. The results show that the time consumption of attitude adjustment is effectively reduced for the free-falling robot by the proposed method. © 2017, Science Press. All right reserved.