用高斯混合模型建模滑移机器人运动模式，提升状态估计精度。
Characterizing gaussian mixture of motion modes for skid-steer vehicle state estimation
- 基于高斯混合模型识别多种运动模式，构建分段局部模型。
- 在中等尺寸滑移机器人上实现角速度状态估计，提升实时性与准确性。
- 适合需要高精度运动建模的移动机器人控制与状态估计场景。
滑移转向轮式机器人（SSWMRs）依赖轮胎与地面的滑移实现运动，但缺乏可靠的摩擦模型导致运动模型不可靠，尤其在用于状态估计和控制的简化模型中更为明显。为解决此问题，本文采用基于高斯混合模型的集成建模范式，将整体运动模型分解为多个局部模型，以分散性能与资源需求，并实现快速实时预测。该方法被整合进交互多模型（IMM）状态估计算法中，以角速度为待估状态，在中等尺寸滑移转向轮式机器人平台上进行了验证。
原文摘要 · Abstract (English)
Skid-steered wheel mobile robots (SSWMRs) are characterized by the unique domination of the tire-terrain skidding for the robot to move. The lack of reliable friction models cascade into unreliable motion models, especially the reduced ordered variants used for state estimation and robot control. Ensemble modeling is an emerging research direction where the overall motion model is broken down into a family of local models to distribute the performance and resource requirement and provide a fast real-time prediction. To this end, a gaussian mixture model based modeling identification of model clusters is adopted and implemented within an interactive multiple model (IMM) based state estimation. The framework is adopted and implemented for angular velocity as the estimated state for a mid scaled skid-steered wheel mobile robot platform.
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