A study of modeling signal dependent noise in robotic systems
Date of Issue2017
School of Mechanical and Aerospace Engineering
Robotic systems are stochastic in nature. That is, there is an inherent error associated with motions commanded to the robot and those executed by the robot. Further, this error itself is a random variable with a particular probability distribution. In this project, we aim to characterize the uncertainty model associated with a given mobile robot. Specifically, we investigate whether signal-dependent noise model where the uncertainty depends on the robot’s motion and control inputs are a more suitable as compared to constant additive noise model. The formulation used in the parameterizes the uncertainty and recovers the parameters through maximization of log-likelihood of the state or measurement trajectory. We used Clearpath Ridgeback mobile robot as the experimental platform in the project.
Final Year Project (FYP)
Nanyang Technological University