Online identification of skidding modes with interactive multiple model estimation

TL;DR

This paper introduces an IMM-based filtering framework for probabilistically identifying discrete skidding modes in wheel mobile robots, aiding in motion prediction and diagnostics in complex outdoor environments.

Contribution

It presents a novel application of interactive multiple model filtering to identify robot operation modes caused by terrain changes or wheel traction loss.

Findings

Successfully distinguishes different skidding modes in outdoor terrains.

Enhances motion prediction accuracy for wheel mobile robots.

Supports better maintenance and control decisions.

Abstract

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.