Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean
(Sub-T) environments in the presence of aerosol particles have recently become
the main focus in the field of robotics. Aerosol particles such as smoke and
dust directly affect the performance of any mobile robotic platform due to
their reliance on their onboard perception systems for autonomous navigation
and localization in Global Navigation Satellite System (GNSS)-denied
environments. Although obstacle avoidance and object detection algorithms are
robust to the presence of noise to some degree, their performance directly
relies on the quality of captured data by onboard sensors such as Light
Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel
modular agnostic filtration pipeline based on intensity and spatial information
such as local point density for removal of detected smoke particles from Point
Cloud (PCL) prior to its utilization for collision detection. Furthermore, the
efficacy of the proposed framework in the presence of smoke during multiple
frontier exploration missions is investigated while the experimental results
are presented to facilitate comparison with other methodologies and their
computational impact. This provides valuable insight to the research community
for better utilization of filtration schemes based on available computation
resources while considering the safe autonomous navigation of mobile robots.

提出一种基于强度和空间信息的新型模块化通用滤波流程，用于消除探测到的烟雾颗粒物，实现移动机器人在困难和无结构的亚地下环境中的安全自主导航。

自主异构机器人系统的高效实时烟雾过滤与搜救

Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue  with Autonomous Heterogeneous Robotic Systems

Collaborative heterogeneous robot systems can greatly improve the efficiency
of target search and navigation tasks. In this paper, we design a heterogeneous
robot system consisting of a UAV and a UGV for search and rescue missions in
unknown environments. The system is able to search for targets and navigate to
them in a maze-like mine environment with the policies learned through deep
reinforcement learning algorithms. During the training process, if two robots
are trained simultaneously, the rewards related to their collaboration may not
be properly obtained. Hence, we introduce a multi-stage reinforcement learning
framework and a curiosity module to encourage agents to explore unvisited
environments. Experiments in simulation environments show that our framework
can train the heterogeneous robot system to achieve the search and navigation
with unknown target locations while existing baselines may not, and accelerate
the training speed.

在未知环境中设计了由无人机和无人地面车组成的异构机器人系统，通过深度强化学习算法学习的策略，该系统能够在迷宫状的矿山环境中搜索目标并导航到目标；通过引入多阶段强化学习框架和好奇心模块，促使机器人探索未访问的环境；在仿真环境中的实验证明我们的框架能够训练异构机器人系统完成未知目标位置的搜索和导航任务，而现有基线模型可能无法做到，并且加速训练速度。