Autonomous Exploration of Challenging Environments

挑战环境的自主探索

• 批准号: 356377-2013
• 负责人: Rekleitis, Ioannis
• 金额: $ 1.09万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634308
• 关键词: Autonomous; Exploration; Challenging; Environments

The problem of exploring an unknown environment is one where robotic research pushes the boundaries of knowledge and technology. Coral reef monitoring with underwater vehicles, planetary exploration, aerial surveys for environmental monitoring or for search and rescue all share some similar characteristics despite the diversity of the domains. In all the above domains, a series of common problems need to be addressed. First, the robot should be able to localize itself by fusing information from proprioceptive sensors, and exteroceptive sensors. Algorithms for estimating the position and orientation (pose) of a mobile robot are considered significant enablers for robot autonomy, and thus, a large spectrum of robotics research has focused on them. The second fundamental problem in autonomous exploration is planning the trajectory of the autonomous vehicle in order to ensure accurate estimation and modeling of the environment while at the same time satisfying the criteria of completeness and efficiency. In recent work I have developed an algorithm for the complete visual coverage of known environment using an unmanned aerial vehicle in an efficient manner. In addition my work on balancing exploitation and exploration, that is the trade-offs between efficiency and accuracy, which was developed for indoor environments using a graph representation, would be extended for the underwater domain.The above components would enable robots to accurate collect sensory information from the underwater domain in a systematic and efficient manner. Underwater environments are particularly challenging due to limited visibility, locally self-similar structures, and the unpredictability of motions due to surge and currents. The final part of the proposed research would be to develop off-line bundle-adjustment based algorithms for the accurate reconstruction of environmental models combining position, intensity, and color information for used by scientist in the fields of marine biology and planetary exploration [J3]. In particular, autonomous operations over coral reefs would benefit greatly from accurate localization algorithms.

探索未知环境的问题是机器人研究突破知识和技术界限的问题。尽管领域多种多样，但水下航行器的珊瑚礁监测、行星探索、环境监测或搜索和救援的航空勘测都具有一些相似的特征。在上述所有领域中，都需要解决一系列常见问题。首先，机器人应该能够通过融合本体感受传感器和外感受传感器的信息来定位自身。用于估计移动机器人位置和方向（位姿）的算法被认为是机器人自主性的重要推动因素，因此，大量的机器人研究都集中在它们上。自主探索的第二个基本问题是规划自主车辆的轨迹，以确保对环境的准确估计和建模，同时满足完整性和效率的标准。在最近的工作中，我开发了一种算法，可以使用无人机以有效的方式对已知环境进行完整的视觉覆盖。此外，我在平衡开发和探索方面所做的工作，即效率和准确性之间的权衡，是使用图形表示为室内环境开发的，将扩展到水下领域。上述组件将使机器人能够以系统和高效的方式准确地从水下领域收集感知信息。由于能见度有限、局部自相似结构以及浪涌和水流引起的运动的不可预测性，水下环境尤其具有挑战性。该研究的最后一部分是开发基于离线束调整的算法，用于结合位置、强度和颜色信息精确重建环境模型，供海洋生物学和行星探索领域的科学家使用[J3]。特别是，珊瑚礁上的自主操作将大大受益于准确的定位算法。