Active Sampling Planning by Attention-Based Deep Neural Network for Environmental Field Mapping.

Regarding the problem of environmental field reconstruction, a method is proposed to establish the optimal observation position of the environmental field using a random process and complete the online planning of the optimal observation position through deep learning technology. First, this chapter uses a random process method to construct an environmental field model and describes the environmental object as a Gaussian Markov process. For the variable spatial field random process model, the information gain evaluation mechanism is used to define the sampling point with the most information in the field as the approximate optimal observation position, and the global optimal observation point is generated. Subsequently, this chapter proposes a high-precision fitting representation and real-time calculation framework based on a deep attention network, constructs a deep neural network with complex nonlinear representation learning ability, learns the relationship between the covariance function corresponding to the Gaussian random field and the optimal information observation point, simulates the global optimal observation position generated by the random process environment model, solves the real-time calculation of the global optimal position, and realizes the online representation and planning of the spatial field observation position.