Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot (AWMR) in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design, evaluation, optimization, and motion control of AWMRs. In this paper, decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction. Numerical analysis of traction performance in terms of drawbar pull, tractive efficiency, and driving torque is carried out for wheels of different radii, widths, and lug heights, under different wheel slips. Effects of normal forces on wheels are analyzed. Results presented in figures are discussed and used to draw some conclusions. Furthermore, a multiobjective optimization (MOO) method for achieving optimal mobility is presented. The MOO problem is formulated based on five independent variables including wheel radius r, width b, lug height h, wheel slip s, and wheel rotation angle θ with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio. Genetic algorithm in MATLAB is used to obtain optimized wheel design and traction control parameters such as drawbar pull, tractive efficiency, and dynamic traction ratio required for good mobility performance. Comparison of MOO results with experimental results shows a good agreement. A method to apply the MOO results for online traction and mobility prediction and control is discussed.