Modern designs of micro air vehicles (MAVs) are mostly inspired by nature's flyers, such as hummingbirds and flying insects, which results in the birth of bio-inspired MAVs. The history and recent progress of the aerodynamic mechanisms in bio-inspired MAVs are reviewed in this study, especially focused on those compound layouts using bio-inspired unsteady aerodynamic mechanisms. Several successful bio-mimicking MAVs and the unsteady high lift mechanisms in insect flight are briefly revisited. Four types of the compound layouts, i.e. the fixed/flapping-wing MAV, the flapping rotary wing MAV, the multiple-pair flapping-wing MAV, and the cycloidal rotor MAV are introduced in terms of recent findings on their aerodynamic mechanisms. In the end, future interests in the field of MAVs are suggested. The authors' review can provide solid background knowledge for both future studies on the aerodynamic mechanisms in bio-inspired MAVs and the practical design of a bio-inspired MAV.

Optimal motion planning becomes more and more important these days and is critical for motion planning algorithms developed in the academia to be applicable to real-world applications. The objective of this article is to survey the current state-of-the-art on optimal motion planning algorithms in terms of their three main components: the decision variables and problem formulation, constraints, and objectives. A selection of proposed techniques is reviewed along with a discussion of their effectiveness and limitations. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system-level design choices.

The free-surface flows, such as flows in rivers, lakes, and tides, play an important role in hydraulic engineering and environmental monitoring. However, due to their complex and changeable characters, the precise evolution procedure is quite difficult to reconstruct. In this study, the authors propose a novel framework to reconstruct the free-surface flow modelled by the shallow water equations by assimilating the images sequences. In particular, the ensemble Kalman filter framework is employed to implement the assimilation process. The efficiency of the proposed strategy has been verified through numerical simulations in which the accurate flow field in different situations could be obtained within limited assimilation steps.

The authors propose a novel reinforcement learning (RL) framework, where agent behaviour is governed by traditional control theory. This integrated approach, called time-in-action RL, enables RL to be applicable to many real-world systems, where underlying dynamics are known in their control theoretical formalism. The key insight to facilitate this integration is to model the explicit time function, mapping the state-action pair to the time accomplishing the action by its underlying controller. In their framework, they describe an action by its value (action value), and the time that it takes to perform (action time). An action-value results from the policy of RL regarding a state. Action time is estimated by an explicit time model learnt from the measured activities of the underlying controller. RL value network is then trained with embedded time model to predict action time. This approach is tested using a variant of Atari Pong and proved to be convergent.

Thrust generation is a crucial aspect of fish locomotion that depends on a variety of morphological and kinematic parameters. In this work, the kinematics of caudal fin motion of a robotic fish are optimised experimentally. The robotic fish actuates its caudal fin with flapping and rotation motion, and also measures the fin hydrodynamic force and torque. Total nine designs of the caudal fins are investigated, with three different shapes (or inclination angles) and three stiffness. The optimisation is based on a policy search (PS) algorithm, which is used to maximise the thrust-generation efficiency of the caudal fins. The authors first parametrise fin spanwise-rotation as a sinusoidal function using rotation amplitude and phase delay and test whether it is beneficial to thrust-generation efficiency. The result shows that the rotation does not contribute to the efficiency, as the efficiency is maximised at zero amplitude. Next, the authors optimise flapping amplitude and trajectory profile without fin rotation. Results show that smaller flapping amplitude results in higher efficiency and linear flapping trajectories are preferred over sinusoidal ones. Fins that have the highest flexibility are more efficient in thrust generation although they generate less thrust, while an inclination angle of 30° yields the most efficient fin shape.