Level Curve Tracking without Localization Enabled by Recurrent Neural Networks
2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE)
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.
National Science Foundation
Kalman filtering, level curve tracking, long short-term memory
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