Depth completion and object detection are two crucial tasks often used for
aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial
Vehicles (UAVs). Common solutions include using measurements from a LiDAR
sensor; however, the generated point cloud is often sparse and irregular and
limits the system's capabilities in 3D rendering and safety-critical
decision-making. To mitigate this challenge, information from other sensors on
the UAV (viz., a camera used for object detection) is utilized to help the
depth completion process generate denser 3D models. Performing both aerial
depth completion and object detection tasks while fusing the data from the two
sensors poses a challenge to resource efficiency. We address this challenge by
proposing a novel approach to jointly execute the two tasks in a single pass.
The proposed method is based on an encoder-focused multi-task learning model
that exposes the two tasks to jointly learned features. We demonstrate how
semantic expectations of the objects in the scene learned by the object
detection pathway can boost the performance of the depth completion pathway
while placing the missing depth values. Experimental results show that the
proposed multi-task network outperforms its single-task counterpart,
particularly when exposed to defective inputs.

该论文提出了一种基于多任务学习的编码器模型的方法，通过特征共享来同时进行航空深度完成和目标检测，进而提高无人机 3D 建图的资源利用效率，该方法实验结果表明在特定情况下，比单任务的深度完成算法表现更好。

目标语义学赋予我们所需的深度：多任务方法实现地面深度补全

Object Semantics Give Us the Depth We Need: Multi-task Approach to  Aerial Depth Completion

Remain Well Clear, keeping the aircraft away from hazards by the appropriate
separation distance, is an essential technology for the safe operation of
uncrewed aerial vehicles in congested airspace. This work focuses on automating
the horizontal separation of two aircraft and presents the obstacle avoidance
problem as a 2D surrogate optimization task. By our design, the surrogate task
is made more conservative to guarantee the execution of the solution in the
primary domain. Using Reinforcement Learning (RL), we optimize the avoidance
policy and model the dynamics, interactions, and decision-making. By
recursively sampling the resulting policy and the surrogate transitions, the
system translates the avoidance policy into a complete avoidance trajectory.
Then, the solver publishes the trajectory as a set of waypoints for the
airplane to follow using the Robot Operating System (ROS) interface. The
proposed system generates a quick and achievable avoidance trajectory that
satisfies the safety requirements. Evaluation of our system is completed in a
high-fidelity simulation and full-scale airplane demonstration. Moreover, the
paper concludes an enormous integration effort that has enabled a real-life
demonstration of the RL-based system.

本文使用强化学习算法，最小化无人机避障中的水平分离问题，将避障策略转化为一组航迹点，以满足安全需求，并在高保真模拟和实际飞行演示中进行了评估。

基于强化学习的空中交通冲突解除

Reinforcement Learning-Based Air Traffic Deconfliction

The dangers of adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents
operating in public are increasing. Adopting AI-based techniques and, more
specifically, Deep Learning (DL) approaches to control and guide these UAVs can
be beneficial in terms of performance but can add concerns regarding the safety
of those techniques and their vulnerability against adversarial attacks.
Confusion in the agent's decision-making process caused by these attacks can
seriously affect the safety of the UAV. This paper proposes an innovative
approach based on the explainability of DL methods to build an efficient
detector that will protect these DL schemes and the UAVs adopting them from
attacks. The agent adopts a Deep Reinforcement Learning (DRL) scheme for
guidance and planning. The agent is trained with a Deep Deterministic Policy
Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that
utilises Artificial Potential Field (APF) to improve training times and
obstacle avoidance performance. A simulated environment for UAV explainable
DRL-based planning and guidance, including obstacles and adversarial attacks,
is built. The adversarial attacks are generated by the Basic Iterative Method
(BIM) algorithm and reduced obstacle course completion rates from 97\% to 35\%.
Two adversarial attack detectors are proposed to counter this reduction. The
first one is a Convolutional Neural Network Adversarial Detector (CNN-AD),
which achieves accuracy in the detection of 80\%. The second detector utilises
a Long Short Term Memory (LSTM) network. It achieves an accuracy of 91\% with
faster computing times compared to the CNN-AD, allowing for real-time
adversarial detection.

采用可解释的深度学习方法为指导和规划的无人机构建了一个仿真环境，其中包括障碍和对抗性攻击，并建立了对此的对抗性攻击检测器