LSTM-enabled level curve tracking in scalar fields using multiple mobile robots
Proceedings of the ASME Design Engineering Technical Conference
In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile robots. We design and implement a long short term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track desired level curves. Based on the existing work of cooperative Kalman filter, we design an LSTM-enhanced Kalman filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimate the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. The LSTM enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. Another benefit is that we can train using larger resources to get more accurate models, while utilizing a limited number of resources when the mobile sensor network is deployed in production. Simulation results show that this LSTM enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network.
National Science Foundation
Kunj J. Parikh and Wencen Wu. "LSTM-enabled level curve tracking in scalar fields using multiple mobile robots" Proceedings of the ASME Design Engineering Technical Conference (2021). https://doi.org/10.1115/DETC2021-68554