This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

In recent years, the air-drop launch technology of near-space UAVs has attracted much attention. Between downfall from the carrier and the flight control system’s initiation, the UAV presents free-fall movement. This free-fall process is very important for the control effect of the flight control system and is also crucial for the safety of the UAV and the carrier. Focus is required on two important dynamic parameters of the UAV: the moment of inertia and the center of mass position. In this paper, we used a quasi-steady model proposed by predecessors to address the flat-plate falling problem with modifications to describe the freely falling motion of the wing. Computational fluid dynamics (CFD) were used to simulate the free-fall movement of the wing with various parameters, and the wing release behavior was analyzed to check the quasi-steady model. Research shows that the movement characteristics of the falling wing are mostly reflected in the longitudinal plane, and the developed quasi-steady analytical model can more accurately describe the dynamic behavior of free-fall to some extent. By using CFD methods, we further investigated the aerodynamic performance of the free-fall wing. The results show that the wing mainly presents tumbling and fluttering motion. Changing the moment of inertia around the tumbling axis changes the tumbling frequency and the time point as the wing enters tumbling. In contrast, changing the position of the center of mass significantly changes the form of falling and makes the free-fall motion more complex. Therefore, it is necessary to carefully configure the center of mass in the UAV design process.

Electric short takeoff and landing (eSTOL) aircraft utilize the slipstream generated by distributed propellers to significantly increase the effective lift coefficient and reduce the takeoff and landing distances. By utilizing the blown lift, eSTOL UAVs can achieve similar takeoff and landing site requirements as electric vertical takeoff and landing (eVTOL) UAVs, while having lower takeoff and landing energy consumption and thrust requirements. This research proposes a high-peak-power distributed electric propulsion (DEP) system model and overload design method for eSTOL UAVs to further improve the power and thrust of the propulsion system. The model considers motor temperature factors with the throttle input, which is solved through three-round iterative calculations. The experimental and simulation results indicate that the maximum error of the high-peak-power propulsion unit model without considering temperature is 19.52%, and the maximum error when considering temperature is 1.2%. The propulsion unit ground test indicates that the main factors affecting peak power are the duration of peak power and the temperature limit of the motor. Finally, the effectiveness of the propulsion system model is verified through ground tests and UAV flight tests.