PDF(Pubmed)
Abstract：
The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.
摘要：
通过开发多惯性导航系统（M-INS）解决了移动机器人精确动态定位的挑战。传统的单惯性导航系统(INSs)在动态条件下普遍存在的固有累积传感器误差,在预定义的平面配置中集成多个INS单元，利用单位之间的固定距离作为不变约束。采用扩展卡尔曼滤波器(EKF)来显著提高定位精度。所提出的3INSEKF算法的动态实验验证揭示了对单个INS单元的显着改进，随着定位误差的减少和稳定性的增加，导致平均准确度提高率超过60%。这一进步对于要求高精度的移动机器人应用尤为重要。如自动驾驶和灾难搜救。这项研究的发现不仅证明了M-INS提高动态定位精度的潜力，而且为机器人导航系统的未来发展提供了新的研究方向。
