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Yating Yuan | Applied Mathematics, University of Waterloo
Planning and Control as Probabilistic Inference on Factor Graphs
Planning and optimal control under uncertainty are central problems in the field of autonomous robotics. While planning methods or optimal control algorithms as probabilistic inferences have been separately developed over the past decades, our main purpose here is to present a unified framework for planning collision-free trajectories and finding the optimal controls together via probabilistic inference. In this framework, Gaussian Processes (GPs) will be used to represent continuous-time trajectories, the optimal problem can be defined as a distribution over trajectories and controls and be treated as a maximum a posterior (MAP) estimation problem. Moreover, with Bayes’ law and factor graphs, the MAP can be factorized and transformed into a least squares problem that can be solved easily by numerical tools, such as Gaussian-Newton or Levenberg-Marquardt methods. We conduct comparison experiments to show the feasibility of the proposed framework.