Applications of free-flying robots range from entertainment purposes to aerospace applications. The control algorithm for such systems requires accurate estimation of their states based on sensor feedback. The objective of this paper is to design and verify a lightweight state estimation algorithm for a free-flying open kinematic chain that estimates the state of its center-of-mass and its posture. Instead of utilizing a nonlinear dynamics model, this research proposes a cascade structure of two Kalman filters (KF), which relies on the information from the ballistic motion of free-falling multibody systems together with feedback from an inertial measurement unit (IMU) and encoders. Multiple algorithms are verified in the simulation that mimics real-world circumstances with Simulink. Several uncertain physical parameters are varied, and the result shows that the proposed estimator outperforms EKF and UKF in terms of tracking performance and computational time.
翻译:自由飞行机器人的应用范围从娱乐目的到航空航天应用不等。这种系统的控制算法要求根据传感器反馈准确估计其状态。本文的目的是设计和核查自由飞行的开放运动链的轻量国家估计算法,该算法估计其质量中心状态和态势。这项研究不使用非线性动态模型,而是建议采用两个卡尔曼过滤器(KF)的级联结构,该结构依靠自由降落的多体系统的弹道运动以及惯性测量单位和编码器的反馈。多种算法在模拟模拟Simmlink模拟现实世界环境时得到验证。若干不确定的物理参数各不相同,结果显示,在跟踪性能和计算时间方面,拟议的估计器优于EKF和UKF。