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Environmental Estimation and Smoothing Algorithms for Dynamic and Modular Robots

出版	ProQuest Information and Learning, 2016
URL	http://books.google.com.hk/books?id=yqGUswEACAAJ&hl=&source=gbs_api

註釋A set of estimation algorithm is presented for various robotics platforms. Among many different types of platforms, this work focuses on two types; large, dynamically complex systems with rich set of sensors and high computation power, and self-reconfigurable modular robots with limited computational resources and sensing capability. Three distinct types of estimation algorithm are presented; 1) adaptive Gaussian mixture smoother for highly complex nonlinear dynamics and measurement models with multi-modal noises, 2) a scalable and efficient surface normal estimation algorithms for computationally limited platforms, and 3) a set of estimation algorithms to calibrate a set of modular robots in order to estimate position and orientation of sensor module. Each set of algorithms are validated with appropriate numerical studies and experiments.