Flying vertebrates exhibit sophisticated wingbeat kinematics. Their specialized forelimbs allow for the wing morphing motion to couple with the flapping motion during their level flight, Previous flyable bionic platforms have successfully applied bio-inspired wing morphing but cannot yet be propelled by the morphing-coupled wingbeat pattern. Spurred by this, we develop a bio-inspired flapping-wing aerial vehicle (FWAV) entitled RoboFalcon, which is equipped with a novel mechanism to drive the bat-style morphing wings, performs a morphing-coupled wingbeat pattern, and overall manages an appealing flight. The novel mechanism of RoboFalcon allows coupling the morphing and flapping during level flight and decoupling these when maneuvering is required, producing a bilateral asymmetric downstroke affording high rolling agility. The bat-style morphing wing is designed with a tilted mounting angle around the radius at the wrist joint to mimic the wrist supination and pronation effect of flying vertebrates' forelimbs. The agility of RoboFalcon is assessed through several rolling maneuver flight tests, and we demonstrate its well-performing agility capability compared to flying creatures and current flapping-wing platforms. Wind tunnel tests indicate that the roll moment of the asymmetric downstroke is correlated with the flapping frequency, and the wrist mounting angle can be used for tuning the angle of attack and lift-thrust configuration of the equilibrium flight state. We believe that this work yields a well-performing bionic platform and provides a new actuation strategy for the morphing-coupled flapping flight.

Abstract Since the limitation on the onboard equipment, the sparse stereo vision is becoming a suitable choice for the deployment of micro air vehicles (MAV) and small robots. However, for the point feature‐based sparse stereo, most of the current stereo algorithms ignore the similarity between feature points, so it is hard to achieve high accuracy. In addition, the problem of clustered feature distribution will still affect the performance of point feature‐based algorithms in the application. To make up for these deficiencies, the authors propose an improved features from accelerated segment test (FAST) feature detector to suppress the point detection in complex texture regions. Most importantly, the authors present a novel census transform (CT)‐based algorithm that contains two encoders ‘texture orientation’ and ‘texture gradient’ to get a more efficient census bit string for the feature point. Instead of randomly selecting pixels to calculate the bit string, we combine the texture characteristics of the census windows where feature points are located. Compared with the original CT, the processing speed of our method is improved, and the average error of our method is reduced by 18.05%. The evaluation results show the presented improved point feature‐based sparse stereo algorithm has a great value in engineering applications.

In this paper, a bio-inspired monocular vision perception method combined with a learning-based reaction local planner for obstacle avoidance of micro UAVs is presented. The system is more computationally efficient than other vision-based perception and navigation methods such as SLAM and optical flow because it does not need to calculate accurate distances. To improve the robustness of perception against illuminance change, the input image is remapped using image moment which is independent of illuminance variation. After perception, a local planner is trained using deep reinforcement learning for mapless navigation. The proposed perception and navigation methods are evaluated in some realistic simulation environments. The result shows that this light-weight monocular perception and navigation system works well in different complex environments without accurate depth information.

For the rising hazard of pirate attacks, unmanned aerial vehicle (UAV) swarm monitoring is a promising countermeasure. Previous monitoring methods have deficiencies in either adaptivity to dynamic events or simple but effective path coordination mechanisms, and they are inapplicable to the large-area, low-target-density, and long-duration persistent counter-piracy monitoring. This article proposes a self-organized UAV swarm counter-piracy monitoring method. Based on the pheromone map, this method is characterized by (1) a reservation mechanism for anticipatory path coordination and (2) a ship-adaptive mechanism for adapting to merchant ship distributions. A heuristic depth-first branch and bound search algorithm is designed for solving individual path planning. Simulation experiments are conducted to study the optimal number of plan steps and adaptivity scaling factor for different numbers of UAVs. Results show that merely decreasing revisit intervals cannot effectively reduce pirate attacks. Without the ship-adaptive mechanism, the proposed method reduces up to 87.2%, 43.2%, and 5.5% of revisit intervals compared to the Lèvy Walk method, the sweep method, and the baseline self-organized method, respectively, but cannot reduce pirate attacks; while with the ship-adaptive mechanism, the proposed method can reduce pirate attacks by up to 6.7% compared to the best of the baseline methods.