LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots
LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots
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DOI：
10.1115/detc2021-68554
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发表时间：
2021-08
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影响因子：
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通讯作者：
Kunj J. Parikh;Wencen Wu
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作者：
Kunj J. Parikh;Wencen Wu
在这项工作中，我们调查的水平曲线跟踪问题，在未知的标量场使用有限数量的移动的机器人。我们设计并实现了一个长短期记忆（LSTM）启用控制策略的移动的传感器网络，以检测和跟踪所需的水平曲线。基于现有的合作卡尔曼滤波器的工作，我们设计了一个LSTM增强卡尔曼滤波器，利用传感器的测量和过去的字段和梯度的序列来估计当前字段值和梯度。我们还设计了一个LSTM模型来估计场的Hessian。启用LSTM的策略具有一些好处，例如它可以在部署之前在已知字段中的水平曲线集合上进行离线训练，其中训练的模型将使移动的传感器网络能够跟踪未知字段中的水平曲线以用于各种应用。另一个好处是，我们可以使用更大的资源进行训练，以获得更准确的模型，同时在生产中部署移动的传感器网络时利用有限数量的资源。仿真结果表明，这种LSTM使控制策略成功地跟踪水平曲线使用移动的多机器人传感器网络。
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.