摘要
机器人在未知复杂环境下作业时,有从高处跌落的风险,所以需考虑空中姿态调整能力,减轻由错误的着陆方式造成的伤害。针对机器人空中不能借助外力翻转的难点,仿照猫从空中跌落可安全着陆特性,研究仿落猫机器人空中姿态调整的最优下落轨迹问题。首先利用可以扭转的轴对称双刚体模型导出机器人的数学方程,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。由于系统具有高度非线性和强耦合,且传统方法需要提供目标函数不一定有的梯度信息,所以采用粒子群优化算法寻找以系统关节耗能为指标的目标函数最优解。最后实施数值仿真,结果显示改进算法对于落猫机器人轨迹规划是有效的。
A robot may be at the risk of falling from a high place when it works in an unknown complex environ- ment, so the attitude control ability of the robot should be considered to reduce the damage caused by the wrong drop- ping posture. In terms of the hard task that there are no external forces that tend to rotate a robot' s body, we studied the optimal falling trajectory of attitude adjustment in the air of the falling cat robot problem based on imitating the cat' s feature that it can land on safety when it drops from upper air. Firstly, we formulated a mathematical equation based on the twisted model of two identical axially symmetric rigid bodies, owing to the nonintegrable angular veloci- ty, the problem of attitude control of the falling cat robot could be transformed to the problem of a nonholonomic mo- tion planning. We found the optimal solution of the objective function that taking the energy cost of the joint of the system as the index by particle swarm optimization, because the system is highly nonlinear and strong coupling, the traditional methods need some gradient message which objective function may have not. Finally, we carried out the numerical simulation, the results show that the algorithm is an effective one for trajectory planning of the falling cat robot.
出处
《计算机仿真》
北大核心
2017年第5期357-361,419,共6页
Computer Simulation
基金
国家自然科学基金资助项目(51405470)
常州市应用基础研究计划资助项目(CJ20159004)
关键词
落猫机器人
姿态控制
最优轨迹
粒子群优化
Falling cat robot
Attitude control
Optimal trajectory
Particle swarm optimization(PSO)