On-Line Stability Margin and Attitude Estimation for Dynamic Articulating Mobile Robots

Abstract

Stability is an important concern for vehicles which move heavy loads, accelerate or brake aggressively, turn at speed, or operate on sloped terrain. In many cases, vehicles face more than one of these challenges simultaneously. Some are obliged to execute these maneuvers when their high centers of gravity leave them particularly vulnerable to tipover or rollover. A methodology is presented to estimate proximity to tipover for autonomous field robots that must be productive, effective, and self-reliant under such challenging circumstances. While the physical principles governing the computation of stability margin have been known for some time, the realization of these principles in practice raises issues which are at once similar to those of attitude estimation while contrasting heavily with inertial guidance. The problem of stability margin estimation is posed in the fairly general case of accelerated articulating motion over rough terrain. Compatibility with and distinctions from attitude estimation lead to a proposed integrated solution to both problems based on the fusion of inertial, articulation, and terrain relative velocity sensing in an optimal estimation framework. An implementation of a device targeted to an industrial lift truck is presented.