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This paper presents a kinematic extended Kalman filter (EKF) designed to estimate the location of track instantaneous centers of rotation (ICRs) and aid in model‐based motion prediction of skid‐steer robots. Utilizing an ICR‐based kinematic model has resulted in impressive odometry estimates for skid‐steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid‐steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid‐steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of −0.42 m over 40.5 m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000 m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.

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Individuals who work in the field of Prognostic and Health Management (PHM) technology have come to understand that PHM can provide the ability to effectively manage the operation, maintenance and logistic support of individual assets or groups of assets through the availability of regularly updated and detailed health information. Naturally, prospective customers of PHM technology ask, 'How will the implementation of PHM benefit my organization?' Typically, the response by individuals in the field is, 'Anecdotal evidence indicates that PHM decreases maintenance costs, increases operational availability and improves safety'. This information helps the prospective customer understand the practical benefits of the technology but that customer stills needs more information to justify their investment in the technology. The customer needs a calculated return on investment (ROI) figure for their particular asset that provides financial assessment of the benefit of the investment.The data, time and expertise required to conduct a rigorous cost benefit analysis makes the effort seem daunting to the average engineer with little to no financial analysis training. The reality is that with a cursory understanding of the asset operation, maintenance and logistic issues, a useful cost benefit analysis can be conducted by engineers without business school training.The purpose of this paper is to provide a general methodology for conducting a preliminary cost benefit analysis that calculates an ROI for PHM implementation. The paper will discuss the general types of information needed for the analysis, the quantifying of expected benefits and the types of supporting data required to validate the benefit assumptions as well as an outline for the costing of the PHM technology.

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