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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >Bayesian Mapping-Based Autonomous Exploration and Patrol of 3D Structured Indoor Environments with Multiple Flying Robots
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Bayesian Mapping-Based Autonomous Exploration and Patrol of 3D Structured Indoor Environments with Multiple Flying Robots

机译：基于贝叶斯映射的自主探索和3D结构化室内环境的巡逻，具有多个飞行机器人

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摘要

Mobile robots are frequently faced with mapping and exploring uncertain environments in surveillance, military, and convenience tasks. Often times, human teleoperation is either inconvenient or infeasible for these kinds missions. Furthermore, these tasks can be improved by cooperative multi-agent systems, where coordinating robotic efforts can be complicated and computationally-expensive. This paper presents a stochastic framework for autonomous exploration and patrol with multiple cooperating robots. The first contribution extends the authors' prior work in single-robot exact occupancy grid mapping and autonomous exploration in a 2D environment to mapping and exploring in a 3D environment. The proposed 3D occupancy grid map is computed efficiently using an inverse sensor model that accounts for the sensor uncertainty, where we propose how several measurement sources may be fused together by considering depth readings individually. This approach is scalable to larger and more complex scenarios for real-time mapping. Furthermore, this paper shows how important aspects of a 3D map representing a structured environment are projected onto a 2D occupancy grid map, where an autonomous exploration algorithm is designed to select robotic motions that maximize map information gain. The mapping and exploration algorithms are demonstrated with an experiment where a quadrotor autonomously maps and explores an initially-uncertain environment. The second contribution is a novel approach to multi-vehicle cooperative patrol of environments based on map uncertainty. We propose a cooperative autonomous exploration algorithm, which applies a bidding-based framework to coordinate robotic efforts for improving occupancy grid map information gain. Since these exploration approaches are based on probabilistic knowledge about the map, the 3D occupancy grid map is systematically degraded over time to encourage the robots to revisit regions as time passes, thereby patrolling the environment. Furthermore, using a Bayesian framework and receding horizons, the algorithm is robust to dynamic obstacles within the mapping space. The efficacy of the proposed multi-vehicle cooperative patrol is illustrated with a simulation involving three robots patrolling a large floor plan with a non-cooperative person walking around the space.
机译：移动机器人经常面临着映射和探索监测，军事和便利任务的不确定环境。通常，人类的耳机不方便或对这些任务不方便。此外，这些任务可以通过协作的多种代理系统改进，其中协调机器人努力可以复杂和计算地昂贵。本文提出了一种随机勘探和多种合作机器人巡逻的随机框架。第一款贡献将作者在单机器人的精确占用网格映射和2D环境中的自主探索中的工作扩展到了在3D环境中的映射和探索。所提出的3D占用网格图是有效地使用帐户的逆传感器模型来计算，该逆传感器模型考虑了传感器不确定性，在那里我们提出了如何通过考虑单独考虑深度读数来融合多个测量源。这种方法可扩展到更大且更复杂的实时映射方案。此外，本文示出了表示结构化环境的3D地图的重要方面被投影到2D占用网格图上，其中自主探索算法旨在选择最大化地图信息增益的机器人动作。用实验说明了映射和探测算法，其中四足电池自主地图并探索最初 - 不确定的环境。基于地图不确定性，第二款贡献是一种新的多车型协作巡逻的方法。我们提出了一个合作自主探索算法，该算法应用于基于竞标的框架，以协调用于改善占用网格地图信息增益的机器人努力。由于这些勘探方法基于关于地图的概率知识，因此随着时间的推移，3D占用网格图是系统地降级的，以鼓励机器人作为时间通过，从而巡逻环境。此外，使用贝叶斯框架和后退视野，该算法对映射空间内的动态障碍是强大的。所提出的多载合作巡逻巡逻的功效被示出了涉及三个机器人，其中三个机器人巡逻大楼计划，其中一个不合作的人走在空间周围。

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