Rotation (mathematics)

Related papers: 20

About

Rotation in mathematics refers to the transformation that moves points in a space around a fixed axis or origin while preserving distances and orientation. In robotics and AI, rotation is fundamental to representing and computing the orientation of rigid bodies, coordinate frames, robot joints, and end-effectors in three-dimensional space. It is expressed through multiple mathematical formalisms—including rotation matrices, Euler angles, quaternions, and axis-angle representations—each offering different trade-offs in computational efficiency, singularity avoidance, and interpolation smoothness. Rotation underpins virtually every aspect of robotic systems: solving hand-eye calibration, estimating 6D object pose for manipulation, designing parallel and concentric-tube manipulators, planning humanoid locomotion, and performing SLAM-based navigation. Algorithms for place recognition, image matching, and motion control all depend on accurate rotation estimation and comparison. Metrics for measuring distances between rotations guide optimization in pose graph SLAM and dynamic movement primitives for orientation. Understanding rotation mathematically matters because errors in orientation representation cascade into failures in grasping, navigation, and physical interaction. Choosing the right rotation parameterization—and computing it efficiently and robustly—is therefore a critical engineering decision across nearly all robotics and autonomous systems applications.

Top Cited Papers

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