Autonomous Exploration of Challenging Environments

探索未知环境的问题是机器人研究推动知识和技术边界的问题。尽管领域多种多样，但使用水下航行器进行珊瑚礁监测、行星探索、用于环境监测或搜救的航空勘测都具有一些相似的特征。在所有上述领域中，需要解决一系列共同的问题。首先，机器人应该能够通过融合来自本体感受传感器和外感受传感器的信息来定位自己。用于估计移动的机器人的位置和方向（姿态）的算法被认为是机器人自主性的重要使能器，因此，大量的机器人研究集中在它们上。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，我已经开发出一种算法，在一个有效的方式使用无人驾驶飞行器的已知环境的完整的视觉覆盖。此外，我在平衡开发和探索方面的工作，即效率和准确性之间的权衡，这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.