Level Curve Tracking without Localization Enabled by Recurrent Neural Networks
Publication Date
9-1-2020
Document Type
Conference Proceeding
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
Department
Computer Engineering
Recommended Citation
Ziqiao Zhang, Said Al-Abri, Wencen Wu, and Fumin Zhang. "Level Curve Tracking without Localization Enabled by Recurrent Neural Networks" 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE) (2020): 759-763. https://doi.org/10.1109/CACRE50138.2020.9230272
