AI 中文总结
针对两轮倒立摆机器人非对称操作中的动态不稳定与导航漂移，提出分层控制框架，结合ArUco视觉全局导航与低延迟惯性/编码器局部控制，实验验证了精确导航、抗冲击与稳定负载能力。
AI 中文摘要
我们提出了一种分层控制框架，以解决两轮倒立摆（TWIP）机器人在尝试非对称物体操作时出现的严重动态不稳定性和导航漂移问题。虽然两轮平台具有高度机动性，但其持续的平衡调整使得机载里程计在精确导航方面极不可靠。此外，侧装机械臂的加入在负载被抬起时引入了未驱动的横向侧倾力矩，这一挑战在不平坦地形上更为严峻。为解决这些耦合问题，我们的架构划分了工作量。一个板外视觉系统跟踪头顶的ArUco标记，以提供高延迟的全局航点导航，从而绕过里程计漂移。同时，一个低延迟的板载控制回路利用惯性传感器和编码器数据来抑制主动物理扰动。在我们的物理实验中，这种双回路方法使定制机器人能够精确导航、抑制来自减速带的瞬态冲击、适应动态跷跷板坡道，并在其狭窄的轮距上安全携带负载而不倾倒。
英文摘要
We propose a hierarchical control framework to address severe dynamic instabilities and navigational drift that arise when a two-wheeled inverted pendulum (TWIP) robot attempts asymmetric object manipulation. While two-wheeled platforms are highly manoeuvrable, their constant balancing adjustments make onboard odometry highly unreliable for precise navigation. Furthermore, the addition of a side-mounted robotic arm introduces unactuated lateral roll moments when a payload is lifted, a challenge heavily compounded on uneven terrain. To solve these coupled problems, our architecture divides the workload. An offboard vision system tracks overhead ArUco markers to provide high-latency global waypoint navigation, bypassing odometry drift. Simultaneously, a low-latency onboard control loop rejects active physical disturbances using inertial and encoder data. In our physical experiments, this dual-loop approach enabled the custom-built robot to navigate accurately, reject transient impacts from speed bumps, adapt to a dynamic seesaw ramp, and carry a payload securely without falling over its narrow wheelbase.
Comments6 pages, 9 figures, 2 tables. Accepted for presentation at the 2026 IEEE International Conference on Intelligent Navigation and Systems (CINS), Dubai, UAE
