Autonomous identification and evaluation of safe landing zones are of
paramount importance for ensuring the safety and effectiveness of aerial robots
in the event of system failures, low battery, or the successful completion of
specific tasks. In this paper, we present a novel approach for detection and
assessment of potential landing sites for safe quadrotor landing. Our solution
efficiently integrates 2D and 3D environmental information, eliminating the
need for external aids such as GPS and computationally intensive elevation
maps. The proposed pipeline combines semantic data derived from a Neural
Network (NN), to extract environmental features, with geometric data obtained
from a disparity map, to extract critical geometric attributes such as slope,
flatness, and roughness. We define several cost metrics based on these
attributes to evaluate safety, stability, and suitability of regions in the
environments and identify the most suitable landing area. Our approach runs in
real-time on quadrotors equipped with limited computational capabilities.
Experimental results conducted in diverse environments demonstrate that the
proposed method can effectively assess and identify suitable landing areas,
enabling the safe and autonomous landing of a quadrotor.

本文介绍了一种用于检测和评估四旋翼机器人安全降落潜在地点的新方法，该方法通过整合 2D 和 3D 环境信息，从神经网络 (NN) 获得语义数据以及从视差图获得几何信息，定义了基于几何属性的成本指标，以评估环境中区域的安全性、稳定性和适用性，并实时运行于计算能力有限的四旋翼机器人上，实验证明该方法能够有效评估和识别合适的降落区域，实现四旋翼机器人的安全自主降落。

安全自主四旋翼降落的视觉环境评估

Visual Environment Assessment for Safe Autonomous Quadrotor Landing

Developing aerial robots that can both safely navigate and execute assigned
mission without any human intervention - i.e., fully autonomous aerial mobility
of passengers and goods - is the larger vision that guides the research,
design, and development efforts in the aerial autonomy space. However, it is
highly challenging to concurrently operationalize all types of aerial vehicles
that are operating fully autonomously sharing the airspace. Full autonomy of
the aerial transportation sector includes several aspects, such as design of
the technology that powers the vehicles, operations of multi-agent fleets, and
process of certification that meets stringent safety requirements of aviation
sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague term and
its consequences for researchers and professionals are ambiguous. To address
this gap, we present a comprehensive perspective on the emerging field of
autonomous advanced aerial mobility, which involves the use of unmanned aerial
vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft for
various applications, such as urban air mobility, package delivery, and
surveillance. The article proposes a scalable and extensible autonomy framework
consisting of four main blocks: sensing, perception, planning, and controls.
Furthermore, the article discusses the challenges and opportunities in
multi-agent fleet operations and management, as well as the testing,
validation, and certification aspects of autonomous aerial systems. Finally,
the article explores the potential of monolithic models for aerial autonomy and
analyzes their advantages and limitations. The perspective aims to provide a
holistic picture of the autonomous advanced aerial mobility field and its
future directions.

无人机、自主空中移动、无人机、电动垂直起降、自主高级空中机动能力

自主先进航空机动性 -- 无人机及未来飞行器的端到端自主性框架

Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework  for UAVs and Beyond

Deployment of teams of aerial robots could enable large-scale filming of
dynamic groups of people (actors) in complex environments for novel
applications in areas such as team sports and cinematography. Toward this end,
methods for submodular maximization via sequential greedy planning can be used
for scalable optimization of camera views across teams of robots but face
challenges with efficient coordination in cluttered environments. Obstacles can
produce occlusions and increase chances of inter-robot collision which can
violate requirements for near-optimality guarantees. To coordinate teams of
aerial robots in filming groups of people in dense environments, a more general
view-planning approach is required. We explore how collision and occlusion
impact performance in filming applications through the development of a
multi-robot multi-actor view planner with an occlusion-aware objective for
filming groups of people and compare with a greedy formation planner. To
evaluate performance, we plan in five test environments with complex
multiple-actor behaviors. Compared with a formation planner, our sequential
planner generates 14% greater view reward over the actors for three scenarios
and comparable performance to formation planning on two others. We also observe
near identical performance of sequential planning both with and without
inter-robot collision constraints. Overall, we demonstrate effective
coordination of teams of aerial robots for filming groups that may split,
merge, or spread apart and in environments cluttered with obstacles that may
cause collisions or occlusions.

通过开发具有遮挡感知目标的多机器人多演员视角规划器与贪婪形成规划器比较，我们评估了拍摄人群中碰撞和遮挡对性能的影响，并计划在五个具有复杂多演员行为的测试环境中。与形成规划器相比，我们的顺序规划器在三个场景中为演员生成了 14％更多的视角奖励，并且在其他两个场景上的性能与形成规划相当。总体而言，我们展示了对人群进行拍摄的空中机器人团队的有效协调，这些人群可能分裂、合并或散开，并且在可能引起碰撞或遮挡的障碍物所充斥的环境中。

贪婪视角：多无人机在混乱环境中的协作覆盖视图规划

Greedy Perspectives: Multi-Drone View Planning for Collaborative  Coverage in Cluttered Environments

Aerial operation in turbulent environments is a challenging problem due to
the chaotic behavior of the flow. This problem is made even more complex when a
team of aerial robots is trying to achieve coordinated motion in turbulent wind
conditions. In this paper, we present a novel multi-robot controller to
navigate in turbulent flows, decoupling the trajectory-tracking control from
the turbulence compensation via a nested control architecture. Unlike previous
works, our method does not learn to compensate for the air-flow at a specific
time and space. Instead, our method learns to compensate for the flow based on
its effect on the team. This is made possible via a deep reinforcement learning
approach, implemented via a Graph Convolutional Neural Network (GCNN)-based
architecture, which enables robots to achieve better wind compensation by
processing the spatial-temporal correlation of wind flows across the team. Our
approach scales well to large robot teams -- as each robot only uses
information from its nearest neighbors -- , and generalizes well to robot teams
larger than seen in training. Simulated experiments demonstrate how information
sharing improves turbulence compensation in a team of aerial robots and
demonstrate the flexibility of our method over different team configurations.

本文研究了多飞行器在恶劣气流环境中的运动控制问题，并提出了一种基于深度强化学习和图形卷积神经网络的解决方法，能够帮助机器人团队实现更好的气流补偿和协同运动。