With autonomous aerial vehicles enacting safety-critical missions, such as
the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of
automatically identifying and reasoning about potentially hazardous landing
sites is paramount. This paper presents a coupled perception-planning solution
which addresses the hazard detection, optimal landing trajectory generation,
and contingency planning challenges encountered when landing in uncertain
environments. Specifically, we develop and combine two novel algorithms,
Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision
Trajectory Optimization (Adaptive-DDTO), to address the perception and planning
challenges, respectively. The HALSS framework processes point cloud information
to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target
contingency planner that adaptively replans as new perception information is
received. We demonstrate the efficacy of our approach using a simulated Martian
environment and show that our coupled perception-planning method achieves
greater landing success whilst being more fuel efficient compared to a
nonadaptive DDTO approach.

本论文提出了一种耦合感知规划解决方案，该方案可以自动识别和推理潜在的危险降落点，并在不确定的环境中进行最优着陆轨迹的生成和应急准备规划。

HALO: 自主系统安全着陆优化

HALO: Hazard-Aware Landing Optimization for Autonomous Systems

Reinforcement learning-based path planning for multi-agent systems of varying
size constitutes a research topic with increasing significance as progress in
domains such as urban air mobility and autonomous aerial vehicles continues.
Reinforcement learning with continuous state and action spaces is used to train
a policy network that accommodates desirable path planning behaviors and can be
used for time-critical applications. A Long Short-Term Memory module is
proposed to encode an unspecified number of states for a varying, indefinite
number of agents. The described training strategies and policy architecture
lead to a guidance that scales to an infinite number of agents and unlimited
physical dimensions, although training takes place at a smaller scale. The
guidance is implemented on a low-cost, off-the-shelf onboard computer. The
feasibility of the proposed approach is validated by presenting flight test
results of up to four drones, autonomously navigating collision-free in a
real-world environment.

本文提出了一个基于强化学习的路径规划方法，可以应用到多经纬系统，通过训练一个连续状态和动作的策略网络，使其具有理想的路径规划行为，该方法通过 LSTM 模块来编码不特定数量的状态，可以扩展到具有无限数量代理和维度的情形，同时使用低成本的硬件平台进行实现。实验表明所提出的方法能成功地使 4 个自主导航的飞行器在现实世界环境中无碰撞地导航。

多智能体路径规划中基于长短时记忆的空间编码

Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning

This article analyzes two classes of job selection policies that control how
a network of autonomous aerial vehicles delivers goods from depots to
customers. Customer requests (jobs) occur according to a spatio-temporal
stochastic process not known by the system. If job selection uses a policy in
which the first job (FJ) is served first, the system may collapse to
instability by removing just one vehicle. Policies that serve the nearest job
(NJ) first show such threshold behavior only in some settings and can be
implemented in a distributed manner. The timing of job selection has
significant impact on delivery time and stability for NJ while it has no impact
for FJ. Based on these findings we introduce a methodological approach for
decision-making support to set up and operate such a system, taking into
account the trade-off between monetary cost and service quality. In particular,
we compute a lower bound for the infrastructure expenditure required to achieve
a certain expected delivery time. The approach includes three time horizons:
long-term decisions on the number of depots to deploy in the service area,
mid-term decisions on the number of vehicles to use, and short-term decisions
on the policy to operate the vehicles.

本文分析了控制无人机网络从车站到客户处交付货物的两种工作选择策略类别，重点探讨了此类网络的投递时间和稳定性。基于此发现，我们提出一种方法论的方法来支持决策，考虑到成本和服务品质之间的权衡。