Computer Vision for Autonomous UAV Flight Safety: An Overview and a Vision-based Safe Landing Pipeline Example

Recent years have seen an unprecedented spread of Unmanned Aerial Vehicles (UAVs, or “drones”), which are highly useful for both civilian and military applications. Flight safety is a crucial issue in UAV navigation, having to ensure accurate compliance with recently legislated rules and regulations. The emerging use of autonomous drones and UAV swarms raises additional issues, making it necessary to transfuse safety- and regulations-awareness to relevant algorithms and architectures. Computer vision plays a pivotal role in such autonomous functionalities. Although the main aspects of autonomous UAV technologies (e.g., path planning, navigation control, landing control, mapping and localization, target detection/tracking) are already mature and well-covered, ensuring safe flying in the vicinity of crowds, avoidance of passing over persons, or guaranteed emergency landing capabilities in case of malfunctions, are generally treated as an afterthought when designing autonomous UAV platforms for unstructured environments. This fact is reflected in the fragmentary coverage of the above issues in current literature. This overview attempts to remedy this situation, from the point of view of computer vision. It examines the field from multiple aspects, including regulations across the world and relevant current technologies. Finally, since very few attempts have been made so far towards a complete UAV safety flight and landing pipeline, an example computer vision-based UAV flight safety pipeline is introduced, taking into account all issues present in current autonomous drones. The content is relevant to any kind of autonomous drone flight (e.g., for movie/TV production, news-gathering, search and rescue, surveillance, inspection, mapping, wildlife monitoring, crowd monitoring/management), making this a topic of broad interest.

Deep Reinforcement Learning-Based Accurate Control of Planetary Soft Landing

Planetary soft landing has been studied extensively due to its promising application prospects. In this paper, a soft landing control algorithm based on deep reinforcement learning (DRL) with good convergence property is proposed. First, the soft landing problem of the powered descent phase is formulated and the theoretical basis of Reinforcement Learning (RL) used in this paper is introduced. Second, to make it easier to converge, a reward function is designed to include process rewards like velocity tracking reward, solving the problem of sparse reward. Then, by including the fuel consumption penalty and constraints violation penalty, the lander can learn to achieve velocity tracking goal while saving fuel and keeping attitude angle within safe ranges. Then, simulations of training are carried out under the frameworks of Deep deterministic policy gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor Critic (SAC), respectively, which are of the classical RL frameworks, and all converged. Finally, the trained policy is deployed into velocity tracking and soft landing experiments, results of which demonstrate the validity of the algorithm proposed.

Pilot-Induced Oscillation Prevention During the Aircraft Landing

The paper focuses on a manned aircraft landing control system. It is known that actuator level and rate limitations can cause pilot-induced oscillations. This phenomenon occurs during intensive pilot control in a closed-loop system under certain initiating conditions associated with both the influence of the external environment and changes in the system dynamics. Oscillations appear unintentionally and unexpectedly for the pilot, which jeopardizes flight safety. The study shows the possibility of preventing aircraft oscillations using the method of nonlinear correction of systems by sequential introduction of a pseudo-linear correcting device into the control loop, the phase-frequency characteristic of the device not depending on the amplitude of the input signal. The airplane-pilot closed-loop system for various parameters of the input signal is analyzed by calculating the generalized function of sensitivity and the excitation index. The results of the study are presented in the form of angle and the pitch rate time processes, and landing trajectories.

Research Self-balancing UAV Takeoff and Landing Control System Based on Quadruped Robot

Aiming at the advantages of UAVs in field survey and search as well as their difficulties in taking off and landing in poor ground environment in the field, a simple self-balancing UAV take-off and landing control system based on a quadruped robot is proposed. Firstly, the simple physical model of the system is established and the mathematical analysis is carried out. Secondly, the inverse kinematics of the single leg model is derived. Thirdly, the attitude sensor is used to measure the attitude angle data of the system platform, and the Kalman filter is used in the software design to filter the attitude angle data, and the PID control algorithm is used to control each leg joint. Finally, The design is simulated by MATLAB and experimentally analyzed, and the test results meet the design requirements.