Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Frontiers in Robotics and AI

Jamie Hathaway¹  Alireza Rastegarpanah²  Rustam Stolkin² 

[1] Department of Metallurgy and Materials Science, University of Birmingham, Birmingham, United Kingdom;The Faraday Institution, Harwell Science and Innovation Campus, Didcot, United Kngdom;

关键词: machine learning;    dynamic modeling;    electric vehicles;    cutting;    predictive control;    vision;   

DOI  :  10.3389/frobt.2021.688275

来源: DOAJ

【 摘 要 】

The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of ∼21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

【 授权许可】

Unknown