Autonomous Exploration of Challenging Environments

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。利用水下航行器监测珊瑚礁、行星探索、用于环境监测或搜救的航空勘测都有一些相似的特点，尽管领域各不相同。在上述所有领域，都需要解决一系列共性问题。首先，机器人应该能够通过融合来自本体感知传感器和外部感知传感器的信息来定位自己。移动机器人的位置和姿态估计算法被认为是机器人自主的重要因素，因此，广泛的机器人学研究都集中在这些算法上。自主探索的第二个基本问题是规划自主车辆的轨迹，以确保在满足完整性和效率标准的同时对环境进行准确的估计和建模。在最近的工作中，我开发了一种算法，可以高效地使用无人机对已知环境进行完全可视覆盖。此外，我在开发和探索之间的平衡工作，也就是效率和精度之间的权衡，是使用图形表示为室内环境开发的，将扩展到水下领域。

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.