Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. Firstly, the research is lack of available dataset. Secondly, the sonar image, generally processed at pixel level and transformed to sector representation for the visual habits of human beings, is disadvantageous to the research in artificial intelligence (AI) areas. Towards these challenges, we present a novel dataset, the underwater acoustic target detection (UATD) dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The data was collected from lake and shallow water. To verify the practicality of UATD, we apply the dataset to the state-of-the-art detectors and provide corresponding benchmarks for its accuracy and efficiency.

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In this paper a whole-body control strategy is proposed for walking of humanoid robots. Its basic idea lies in the control of the centre of mass(CoM) with a ZMP regulation as well as the relative pose of the feet of the robot. A stable walking trajectory based on 3D linear inverted pendulum model (3D-LIPM) is planned for the study. Through the proposed study, it is shown that the proposed control strategy perfectly tracks the planned trajectory of CoM and adjusts the ZMP back to the stability area when the robot is out of balance. Simulations results are presented to show the effectiveness of the proposed control scheme.