We reveal the hindwing kinematics of a damselfly that are optimal for the thrust efficiency, which is a major concern of a bio-inspired micro-aerial vehicle. The parameters of the hindwing kinematics include stroke-plane angle, rotational duration, and wing phase. We developed a numerical self-propulsion model to investigate the thrust efficiency. The correlation analysis and optimal analysis were used to investigate the relation between varied hindwing kinematics and thrust efficiency. The results show that the optimal wing kinematics of the hindwing occur at a large stroke-plane angle and a small rotational duration in which the thrust efficiency might increase up to 22% compared with the original motion of the hindwing. The stroke-plane angle is highly positively correlated with thrust efficiency, whereas the rotational duration is moderately negatively correlated; the wing phase has the least correlation. The flow-field analysis indicates that a large stroke-plane angle combined with a small rotational duration has a weak forewing–hindwing interaction, generating a small resulting force on the hindwing, but the force comprises a small negative horizontal force, which hence increases the thrust efficiency. In a flight strategy for a micro-aerial vehicle, a large stroke-plane angle combined with a small rotational duration yields an optimal thrust efficiency, which is suitable for a flight of long duration. A small stroke-plane angle combined with a large rotation is suitable for hovering flight because it leads to a large negative horizontal force and a small vertical force. This work hence provides insight into the design of a tandem-wing micro-aerial vehicle.

The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has not been extensively studied owing to the electrical and mechanical noises generated. In this study, we investigated the characteristics of the EAG mounted on a tethered flying quadcopter and developed a special counter-based algorithm for detecting the odour-generated responses. As the EAG response is negative, the algorithm creates a window and compares the values inside it. Once a value is smaller than the first one, the counter will increase by one and finally turns the whole signal into a clearer odour stimulated result. By experimental evaluation, the new algorithm gives a higher cross-correlation coefficient when compared with the fixed-threshold method. The result shows that the accuracy of this novel algorithm for recognising odour-evoked EAG signals from noise exceeds that of the traditional method; furthermore, the use of insect antennae as odour sensors for flying robots is demonstrated to be feasible.

Understanding the causal impacts among various parameters is essential for designing micro aerial vehicles (MAVs). The simulation of computational fluid dynamics (CFD) provides us with a technique to calculate aerodynamic forces precisely. However, even a single result regularly takes considerable computational time. Machine learning, due to the advance in computer hardware, shows another approach that can speed up the analysis process. In this study, we introduce an artificial neural network (ANN) framework to predict the transient aerodynamic forces and the corresponding energy consumption. Instead of considering the whole transient changes of each parameter as inputs, we utilised the technique of Fourier transform to simplify the ANN structure for minimising the computation cost. Furthermore, two typical activation functions, rectified linear unit (ReLU) and sigmoid, were attempted to build the network. The validity of the method was further examined by comparing it with CFD simulation. The result shows that both functions are able to provide highly accurate estimations that can be implemented for model construction under this framework. Consequently, this novel approach makes it possible to reduce the complexity of analysis, study the flapping wing aerodynamics and enable a more efficient way to optimise parameters.

Recently, facilities with the special intention to help people who lack walking ability or even sit in the wheel chair could be seen everywhere. However, in the hospitals, rehabilitation devices, which are used for people to regain their body functions right after some health problems, are very limited, and even the existing robots have not shown sufficient evidence to prove they are useful, low cost, or easy to handle to be used as a regular rehabilitation method. In this research, a 3-DOF parallel mechanism training device as a compact foot drive mechanism for the gait rehabilitation of walking-disabled patients was developed for solving these sorts of problems. The requirements and constraints were obtained through interviews with medical doctor, physical therapists and related researchers. The composition of this proposed training device is: patients can be supported by a harness system with their feet fastened onto two separated programmable footplates. Based on the preprocessed walking data captured from healthy individuals, reference motion for the footplate can be determined and used to calculate the specifications of the footplate drive mechanism. As the foot drive mechanism, a planar parallel mechanism with 3-DOF was selected. Its dimensional synthesis was carried out taking into consideration the workspace and motion transmissibility, and a prototype of the machine was designed and built in the end. Comparison between the walking data and mechanism ’s analysis indicated that it is possible for the machine to generate natural walking patterns, which will be further proved with a prototype in the future.