US 12,078,972 B2
Model-based control with uncertain motion model
Karl Berntorp, Cambridge, MA (US); and Marcel Menner, Cambridge, MA (US)
Assigned to Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on Mar. 12, 2022, as Appl. No. 17/654,580.
Prior Publication US 2023/0288886 A1, Sep. 14, 2023
Int. Cl. G05B 13/04 (2006.01); G06N 7/01 (2023.01)
CPC G05B 13/048 (2013.01) [G05B 13/042 (2013.01); G06N 7/01 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A probabilistic feedback controller for controlling an operation of a robotic system using a probabilistic filter subject to a structural constraint on an operation of the robotic system, comprising: at least one processor; and a memory having instructions stored thereon that, when executed by the at least one processor, cause the controller to:
collect digital representations of a sequence of measurements of a state of a robotic system at different instances of time indicative of a structural constraint on a shape of a function of a parameter affecting the operation of the robotic system;
execute a probabilistic filter configured to recursively estimate a distribution of a current state of the robotic system given a previous state of the robotic system based on a motion model of the robotic system perturbed by stochastic process noise and a measurement model of the robotic system perturbed by stochastic measurement noise, wherein one or a combination of the motion model, the process noise, the measurement model, and the measurement noise includes the parameter having an uncertainty modeled as a time-varying Gaussian process represented as a weighted combination of time-varying basis functions with weights defined by corresponding Gaussian distributions, such that a time-varying mean of the Gaussian process is a function of the basis functions modified with means of the corresponding Gaussian distributions, and a time-varying variance of the Gaussian process is a function of the basis function modified with variances of the corresponding Gaussian distributions, wherein the probabilistic filter recursively updates both the distribution of the current state of the robotic system and the Gaussian distributions of the weights of the basis functions, and wherein each of the basis function is selected to satisfy the structural constraint indicated by the sequence of measurements of the state of a robotic system; and
execute a control action determined based on an estimate of the distribution of the current state of the robotic system to change the current state of the robotic system according to a control objective.