This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and accelerations in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.

Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition. The proposed architecture is constructed combining dense optical flow approach and auxiliary movement information in video datasets using deep learning methodologies. To the best of our knowledge, this is the first study based on a novel combination of 3D-convolutional neural networks (3D-CNNs) fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition. The contributions of this paper are sixfold. First, a 3D-CNN, also called multiple frames is employed to determine the motion vectors. With the same purpose, the 3D-CNN is secondly used for dense optical flow, which is the distribution of apparent velocities of movement in captured imagery data in video frames. Third, the LSTM is employed as auxiliary information in video to recognize hand-tracking and objects. Fourth, the support vector machine algorithm is utilized for the task of classification of videos. Fifth, a wide range of comparative experiments are conducted on two newly generated chess datasets, namely the magnetic wall chess board video dataset (MCDS), and standard chess board video dataset (CDS) to demonstrate the contributions of the proposed study. Finally, the experimental results reveal that the proposed hybrid deep model exhibits remarkable performance compared to the state-of-the-art studies.

Abstract. Unmanned vehicle systems are becoming increasingly prevalent on the land, in the sea, and in the air. Human-Computer interface design for these systems has a very important role in mission planning. The objective of this work is to design a unmanned sea vehicle and necessary software that can perform off-line path planning, vision management, communication, sensor control, and data management and monitoring of the unmanned sea vehicles.

In this work, the mechatronic based robotic hand is

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