基于交互式多模型估计的滑移模式在线辨识
Online identification of skidding modes with interactive multiple model estimation
AI总结：
本文针对滑移转向轮式移动机器人在复杂室外环境中轮地相互作用难以建模的问题，提出基于交互式多模型（IMM）的滤波框架，以概率方式在线辨识因地形变化或车轮打滑引起的预定运行模式，从而简化运动模型辨识过程。
摘要：
滑移转向轮式移动机器人（SSWMRs）在多种室外环境中运行，其运动行为主要受复杂的轮地相互作用影响。表征这些相互作用至关重要，既关系到机器人即时自主性（用于运动预测与控制），也关系到长期的预测性维护与诊断。理想的解决方案需要获取精确的状态测量以用于决策和控制，但这相当困难，尤其是在这些机器人日益非结构化的室外作业环境中。在此背景下，一个用于辨识预定离散运行模式的框架可以显著简化运动模型辨识过程。为此，我们提出一种基于交互式多模型（IMM）的滤波框架，以概率方式辨识预定义的机器人运行模式，这些模式可能因在不同地形中行驶或车轮牵引力丧失而产生。
英文摘要：
Skid-steered wheel mobile robots (SSWMRs) operate in a variety of outdoor environments exhibiting motion behaviors dominated by the effects of complex wheel-ground interactions. Characterizing these interactions is crucial both from the immediate robot autonomy perspective (for motion prediction and control) as well as a long-term predictive maintenance and diagnostics perspective. An ideal solution entails capturing precise state measurements for decisions and controls, which is considerably difficult, especially in increasingly unstructured outdoor regimes of operations for these robots. In this milieu, a framework to identify pre-determined discrete modes of operation can considerably simplify the motion model identification process. To this end, we propose an interactive multiple model (IMM) based filtering framework to probabilistically identify predefined robot operation modes that could arise due to traversal in different terrains or loss of wheel traction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
