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In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom fa&#x00E7;ade cleaning robot known as the Dual Ascender Robot (DAR). While other fa&#x00E7;ade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most fa&#x00E7;ade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2&#x2013;3 times.

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot