Title

Level Curve Tracking without Localization Enabled by Recurrent Neural Networks

Publication Date

9-1-2020

Document Type

Conference Proceeding

Department

Computer Engineering

Publication Title

2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE)

DOI

10.1109/CACRE50138.2020.9230272

First Page

759

Last Page

763

Abstract

Recursive neural networks can be trained to serve as a memory for robots to perform intelligent behaviors when localization is not available. This paper develops an approach to convert a spatial map, represented as a scalar field, into a trained memory represented by the long short-term memory (LSTM) neural network. The trained memory can be retrieved through sensor measurements collected by robots to achieve intelligent behaviors, such as tracking level curves in the map. Memory retrieval does not require robot locations. The retrieved information is combined with sensor measurements through a Kalman filter enabled by the LSTM (LSTM-KF). Furthermore, a level curve tracking control law is designed. Simulation results show that the LSTM-KF and the control law are effective to generate level curve tracking behaviors for single-robot and multi-robot teams.

Funding Number

1828678

Funding Sponsor

National Science Foundation

Keywords

Kalman filtering, level curve tracking, long short-term memory

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