We develop a new framework for trajectory planning on predefined paths, for general N-link manipulators. Different from previous approaches generating open-loop minimum time controllers or pre-tuned motion profiles by time-scaling, we establish analytic algorithms that recover all initial conditions that can be driven to the desirable target set while adhering to environment constraints. More technologically relevant, we characterise families of corresponding safe state-feedback controllers with several desirable properties. A key enabler in our framework is the introduction of a state feedback template, that induces ordering properties between trajectories of the resulting closed-loop system. The proposed structure allows working on the nonlinear system directly in both the analysis and synthesis problems. Both offline computations and online implementation are scalable with respect to the number of links of the manipulator. The results can potentially be used in a series of challenging problems: Numerical experiments on a commercial robotic manipulator demonstrate that efficient online implementation is possible.
翻译:我们为预设路径,为普通 N- Link 操纵器开发了新的轨迹规划框架。 不同于以往通过时间缩放生成开放环最小时间控制器或预调运动剖面的方法,我们建立了分析算法,在遵守环境制约因素的同时,将所有最初条件恢复到理想目标,在技术上更加相关,我们将相应的州-feback 控制器组合定性为几个理想属性。 我们框架中的一个关键促进因素是引入一个州反馈模板,在由此形成的闭环系统的轨迹之间进行排序。 拟议的结构允许在分析和合成问题中直接在非线性系统上工作。 离线计算和在线执行对于操纵器的链接数量都具有可缩放性。 其结果有可能用于一系列具有挑战性的问题: 商业机器人操纵器的量化实验表明,在线实施效率是可能的。