用交互多模型滤波识别轮式机器人打滑状态，提升复杂地形下的自主控制能力。
Online identification of skidding modes with interactive multiple model estimation
- 基于交互多模型滤波，动态识别机器人在不同地形下的运行模式。
- 可概率性区分打滑与正常行驶状态，支持实时控制决策。
- 适合野外复杂环境下的移动机器人状态监测与故障预警。
差速转向轮式移动机器人（SSWMRs）在多种户外环境中运行，其运动行为受复杂轮地相互作用主导。准确表征这些相互作用对机器人即时自主性（如运动预测与控制）以及长期预测性维护和诊断至关重要。理想的解决方案需要精确的状态测量以支持决策与控制，但在日益非结构化的户外环境中实现这一目标极为困难。在此背景下，预先识别确定的离散运行模式可显著简化运动模型识别过程。为此，我们提出一种基于交互多模型（IMM）的滤波框架，用于概率性识别因穿越不同地形或轮胎牵引力丧失而可能产生的预设机器人运行模式。
原文摘要 · Abstract (English)
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.
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