Feelbert：基于反馈线性化的嵌入式实时四足运动框架
Feelbert: A Feedback Linearization-based Embedded Real-Time Quadrupedal Locomotion Framework
AI总结：
本文提出Feelbert，一种基于反馈线性化、无第三方依赖的嵌入式硬实时四足运动控制框架，结合虚拟轮算法计算落脚点与步态参数，在ROS 2和Raspberry Pi 5上均验证了良好的轨迹跟踪与时间可预测性。
摘要：
四足机器人因其能够将运动适应于一般不平坦地形的能力而变得相当流行。因此，随着时间的推移，人们提出了几种四足运动框架，但很少关注确保控制器具有可预测的定时行为。为了解决这个问题，本工作提出了Feelbert，这是一种用于四足运动的模块化控制框架，适合在硬实时执行约束下的嵌入式系统上执行。它利用反馈线性化控制技术，为机体获得了一个适用于机器人所有配置的闭式控制律。该控制律是在定义了一个适当的刚体模型后推导出来的，该模型使用脚的加速度作为控制变量，而不是估计的接触力。本工作还提供了一种使用虚拟轮概念计算落脚点和步态时间参数的新颖算法，以及一种用于为当前速度命令选择最佳步态调度方案的启发式算法。所提出的框架完全用C++开发，不依赖任何第三方库，也没有动态内存分配，以确保可预测性和实时性能。其实现允许Feelbert既能编译并执行在用于关键应用的嵌入式系统上，也能集成到如Robot Operating System 2（ROS 2）等更大的系统中。因此，Feelbert在这两种场景下都进行了测试，无论是集成到ROS 2中还是作为独立应用程序编译在Raspberry Pi 5上，在参考跟踪和时间可预测性方面都展示了令人满意的结果。
英文摘要：
Quadruped robots have become quite popular for their ability to adapt their locomotion to generic uneven terrains. For this reason, over time, several frameworks for quadrupedal locomotion have been proposed, but with little attention to ensuring a predictable timing behavior of the controller. To address this issue, this work presents Feelbert, a modular control framework for quadrupedal locomotion suitable for execution on an embedded system under hard real-time execution constraints. It leverages the feedback linearization control technique to obtain a closed-form control law for the body, valid for all configurations of the robot. The control law was derived after defining an appropriate rigid body model that uses the accelerations of the feet as control variables, instead of the estimated contact forces. This work also provides a novel algorithm to compute footholds and gait temporal parameters using the concept of imaginary wheels, and a heuristic algorithm to select the best gait schedule for the current velocity commands. The proposed framework is developed entirely in C++, with no dependencies on third-party libraries and no dynamic memory allocation, to ensure predictability and real-time performance. Its implementation allows Feelbert to be both compiled and executed on an embedded system for critical applications, as well as integrated into larger systems such as Robot Operating System 2 (ROS 2). For this reason, Feelbert has been tested in both scenarios, demonstrating satisfactory results both in terms of reference tracking and temporal predictability, whether integrated into ROS 2 or compiled as a standalone application on a Raspberry Pi 5.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
