Smooth extensions of feedback motion planners via reference governors
Smooth extensions of feedback motion planners via reference governors
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通过参考调节器平滑扩展反馈运动规划器
DOI：
10.1109/icra.2017.7989510
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
D. Koditschek
中科院分区：
文献类型：
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作者：
Ömür Arslan;D. Koditschek
在机器人技术中，为近似低阶（例如，位置或速度控制的）机器人模型，然后使这样的参考规划器适应于更精确的高阶（例如，力/转矩控制）机器人模型。在本文中，我们引入了一种新的可证明正确的方法来扩展低阶反馈运动规划器的适用性，高阶机器人模型，同时保持稳定性和避免碰撞的属性，以及执行特定于高阶模型的额外约束。我们的平滑扩展框架利用参考调控器的想法来分离稳定性和约束满足的问题，提供双向耦合的机器人-调控器系统，其中机器人确保相对于调控器的稳定性，而调控器强制执行状态（例如，冲突避免）和控制（例如，致动器限制）约束。我们展示了我们的框架，增强路径规划和矢量场规划的二阶机器人动力学的应用实例。
In robotics, it is often practically and theoretically convenient to design motion planners for approximate low-order (e.g., position-or velocity-controlled) robot models first, and then adapt such reference planners to more accurate high-order (e.g., force/torque-controlled) robot models. In this paper, we introduce a novel provably correct approach to extend the applicability of low-order feedback motion planners to high-order robot models, while retaining stability and collision avoidance properties, as well as enforcing additional constraints that are specific to the high-order models. Our smooth extension framework leverages the idea of reference governors to separate the issues of stability and constraint satisfaction, affording a bidirectionally coupled robot-governor system where the robot ensures stability with respect to the governor and the governor enforces state (e.g., collision avoidance) and control (e.g., actuator limits) constraints. We demonstrate example applications of our framework for augmenting path planners and vector field planners to the second-order robot dynamics.