Aiming at the problem of trajectory tracking between joints of the multi-joint snake-like robot in the flow fields, a trajectory tracking control law proposed based on the improved snake-like curve of a multi-joint snake-like robot to avoid obstacles in the flow fields is studied. Firstly, considering the external disturbance that the fluid environment may impose on the multijoint snake-like robot system, from the point of view of probability, the fluid-solid coupling models of the obstacle channel and multi-joint snake-like robot are established in the flow field by using immersed boundary-lattice Boltzmann method algorithm, which solves the problem of nonlinear fluid motion that cannot be explained by solving the Navier-Stokes (N-S) equation. Then, a potential function is applied to the multi-joint snake-like robot so that the head of the robot can avoid obstacles in the fluid smoothly. By improving the snake-like motion equation, the snake-like curve trajectory tracking function of each joint of the multi-joint snake-like robot with time variation is obtained, which enables the tail joints of the snake-like robot to track the motion trajectory of the head joints. Finally, the effects of different flow field density, velocity, and Reynolds numbers on trajectory tracking of the multi-joint snake-like robot are studied by MATLAB simulations and experiments. The theoretical analysis and numerical simulation show that the designed trajectory tracking control law can make the multi-joint snake-like robot track the trajectory of the front joint when the robot encounters obstacles and make the robot stabilize the lateral distance, longitudinal distance, and direction angle, so as to effectively avoid obstacles. The simulation and experimental results verify the effectiveness of the trajectory tracking control law.
This paper presents a voice activity detection (VAD) approach using convolutive non-negative sparse coding (CNSC) to improve the detection performance in low signal-to-noise (SNR) conditions. Our idea is to use noise-robust feature for speech signal detection while noise is reduced away. We first use magnitude spectrum as the non-negative and additive lowlevel representation of audio signals, and learn a speech dictionary from clean speech as well as a noise dictionary from noise samples. Then, the two dictionaries are concatenated to form a global dictionary, and an audio signal is decomposed into coefficient vectors using CNSC on the global dictionary. Only coefficients corresponding to the bases from the speech dictionary are taken as the features for the signal. At last, the activity labels is given by decoding a conditional random field (CRF) which is constructed to model the context of an audio signal for VAD. Experiments demonstrate that our VAD approach has an excellent performance in low SNR conditions. Index Terms-voice activity detection, convolutive nonnegative sparse coding, conditional random fields
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