This work studies online learningbased trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the underice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyperparameters. The AUV trajectories for sampling are determined on an epochbyepoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyperparameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a longterm reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learningbased online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation resultsmore »
This content will become publicly available on January 1, 2023
Energybased continuous inverse optimal control.
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energybased model, where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an “analysis by synthesis” scheme, which iterates (1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time, and (2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the energybased model simultaneously with a topdown trajectory generator via cooperative learning, where the trajectory generator is more »
 Award ID(s):
 2015577
 Publication Date:
 NSFPAR ID:
 10351397
 Journal Name:
 IEEE transactions on neural networks and learning systems
 ISSN:
 2162237X
 Sponsoring Org:
 National Science Foundation
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