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Underwater gliders are prevailing in oceanic observation nowadays for their flexible deployment and low cost. However, the limited onboard energy constrains their application, hence the motion pattern optimization and energy analysis are the key to maximizing the range of the glider while maintaining the acceptable navigation preciseness of the glider. In this work, a Multi-Objective Artificial Bee Colony (MOABC) algorithm is used to solve the constrained hybrid non-convex multi-objective optimization problem about range and accuracy of gliders in combination with specific glider dynamics models. The motion parameters Pareto front that balances the navigational index referring to range and preciseness are obtained, relevant gliding profile motion results are simulated simultaneously, and the results are compared with the conventional gliding patterns to examine the quality of the solution. Comparison shows that, with the utilization of the algorithm, glider voyage performance with respect to endurance and preciseness can be effectively improved.

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Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.