Computational and experimental study on dynamic behavior of underwater robots propelled by bionic undulating fins

Bionic undulating fins, inspired by undulations of the median and/or paired fin (MPF) fish, have a bright prospective for un-derwater missions with higher maneuverability, lower noisy, and higher efficiency. In the present study, a coupled computa-tional fluid dynamics (CFD) model was proposed and implemented to facilitate numerical simulations on hydrodynamic ef-fects of the bionic undulating robots. Hydrodynamic behaviors of underwater robots propelled by two bionic undulating fins were computationally and experimentally studied within the three typical desired movement patterns, i.e., marching, yawing and yawing-while-marching. Moreover, several specific phenomena in the bionic undulation mode were unveiled and dis-cussed by comparison between the CFD and experimental results under the same kinematics parameter sets. The contributed work on the dynamic behavior of the undulating robots is of importance for study on the propulsion mechanism and control algorithms.

Bionic asymmetry:from amiiform fish to undulating robotic fins

Similar to bionic non-smooth which has been successfully applied in anti-resistance and anti-adhesion, bionic asymmetry is also an inherent property of biological systems and is worth exploring for con-ceivable pragmatic applications. Therefore, bionic asymmetry for undulations is of main interest in this paper. We initially investigate bionic asymmetry with a case study of the undulating robotic fin, RoboGnilos, which evolved from the long dorsal fin of Gymnarchus niloticus in the amiiform mode. Since the performance of the pre-existing undulating fins is hardly satisfactory, we obtain bionic in-spirations of undulatory asymmetry through observations and measurements on the specimen of G. niloticus, to improve upon the performance. Consequently, the newly acquired innovation for bionic asymmetry is incorporated into the previously derived kinematics model, and also applied to the experimental prototype. Both computational and experimental results verify that bionic asymmetric undulation generates better propulsion performance (in terms of linear velocity and efficiency) than the traditional symmetric modes with the same undulatory parameters.

Parallel architecture and optimization for discrete-event simulation of spike neural networks

Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated tasks.However,spike neural networks are usually used on a large scale,and they cannot be computed on commercial,off-the-shelf computers.A parallel architecture is proposed and developed for discrete-event simulations of spike neural networks.Furthermore,mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational analysis.Simulation results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.

Robust optimization of aircraft weapon delivery trajectory using probability collectives and meta-modeling

Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.

CPGs control method using a new oscillator in robotic fish

A new oscillator is presented in this paper based on our pervious oscillator (Zhang’s oscillator). Using this new oscillator, a bionic neural control system, the central pattern generators (CPGs) control system, is built. This control system has a two-level form. To validate the function of this new oscillator and the control system, simulations and experiments were both carried out, a simple robotic fish was built with three joints, and the results showed that the new oscillator can be used in startup and stop control mode, angle offset control mode and amplitude changing control mode. The new oscillator can be used in bionic CPGs control area with a simple form, and may be a new progress in bionic control.