Abstract
In this work, we propose the integration of a bank of Discrete Generalized Proportional Integral Observers (DGPIO) within an Interacting Multiple Model (IMM) structure in order to improve the precision of visual-tracking tasks. Applications such as visual servoing, robotic assisted surgery and optronic weapon systems require accurate and high-precision measurements provided by real-time visual-tracking systems. In this case, the DGPIO-Bank was designed using two kinematic models based in constant velocity (CV) and constant acceleration (CA) motion profiles. The main feature of the DGPIO-Bank is the active disturbance rejection (ADR) feature which reduces noise in the position signal of a moving object. The resultant algorithm uses a fusion of four important features: state interaction, Kalman filtering, active disturbance rejection and multiple models combination. For performance comparison, we evaluated our proposed IMM-DGPIO algorithm and other state of the art IMM algorithms. Experimental results show that our proposed strategy had the best performance.
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The authors would like to thank the Instituto Politécnico Nacional de México and CONACyT de México for their support and funding this project.
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Sánchez-Ramírez, E.E., Rosales-Silva, A.J. & Alfaro-Flores, R.A. High-Precision Visual-Tracking using the IMM Algorithm and Discrete GPI Observers (IMM-DGPIO). J Intell Robot Syst 99, 815–835 (2020). https://doi.org/10.1007/s10846-020-01164-6
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DOI: https://doi.org/10.1007/s10846-020-01164-6