Drones have been increasingly used in various domains, including ecological monitoring in forests. However, the endurance and noise of drones have limited their deployment to short flight missions above canopies. To address these limitations, we introduce ALBERO: a framework comprising a mechanical solution and an optimal planner to realise agile quadrotor perching on tree branches of steep incline. The gripper features an ultra-fast active mechanism inspired by birds' claws that enables quadrotors to perch swiftly on randomly-oriented tree branches. By perching, the drone can preserve energy for extended periods of time, while silently gathering forest data in the canopy. The intrinsic properties of the gripper allow for extra flexibility in size, surface roughness and shape imperfections of natural perches, such as those found in the wild. The gripper also has good scalability properties and can be easily matched to different drones' sizes. The biggest advantage of this novel design lays in its ability to close reactively and ultra-fast (67ms) on the large gripper, 42ms on the small gripper), enabling the quadrotor to perform agile perching manoeuvres from different angles and at different approach speeds. ALBERO's software module comprises of a trajectory planning algorithm adapted for branch perching, ensuring that the drone can perch on inclined cylindrical targets from any starting location in the proximity of the branch. These requirements translate in stringent positioning and orientation accuracy, but they enable the drone to land dynamically from a variety of positions within the forest.

For a small low-cost Unmanned Aerial Vehicle (UAV), the accurate aerodynamics and flight dynamics characteristics wouldn't be obtained easily, and the control coupling is serious, so the robustness of its flight controller must be considered carefully. In order to solve the problem, a Lateral-Directional (Lat-Dir) flight control method based on Deep Reinforcement Learning (DRL) are proposed in this paper. Firstly, based on the nominal state, three control laws are designed: classical Proportional Integral Derivative (PID) control, Linear Quadratic Gaussian (LQG) control based on modern control theory, and Deep Reinforcement Learning (DRL) control based on Twin Delayed Deep Deterministic Policy Gradient (TD3) method. In order to solve the problem of incomprehensible physical meaning of neural network in DRL, a simplified control strategy network is derived based on the inspiration of PID controller. In order to solve the problem that the reward function of DRL is difficult to be determined, the weights of the optimal quadratic function designed by LQG method are adopted, and the weights of control output considering discretization is added also. Then, the three controller are applied to nominal flight state and deviation state respectively, and the numerical flight simulation is carried out. The results show that, in the nominal state, the performance of DRL is close to the LQG and better than the PID. In the deviation state, which the lateral and directional static stable derivatives are changed artificially from stable to neutral stable, the rise time and adjustment time of the DRL change slightly, while the LQG degrades seriously and appears instable, and it is proved that the proposed DRL control method has better performance robustness.