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This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.