Ideally, robots should move in ways that maximize the knowledge gained about the state of both their internal system and the external operating environment. Trajectory design is a challenging problem that has been investigated from a variety of perspectives, ranging from information-theoretic analyses to leaning-based approaches. Recently, observability-based metrics have been proposed to find trajectories that enable rapid and accurate state and parameter estimation. The viability and efficacy of these methods is not yet well understood in the literature. In this paper, we compare two state-of-the-art methods for observability-aware trajectory optimization and seek to add important theoretical clarifications and valuable discussion about their overall effectiveness. For evaluation, we examine the representative task of sensor-to-sensor extrinsic self-calibration using a realistic physics simulator. We also study the sensitivity of these algorithms to changes in the information content of the exteroceptive sensor measurements.

翻译：理想的情况是,机器人应尽量扩大对其内部系统和外部运行环境状况的了解。轨迹设计是一个具有挑战性的问题,从信息理论分析到倾斜法等各种角度都对它进行了调查。最近,提出了基于可观测性的指标,以找到能够快速和准确估计状态和参数的轨迹。文献中尚未很好地理解这些方法的可行性和效力。在本文件中,我们比较了两种最先进的可观测性轨道优化方法,并试图增加重要的理论澄清和关于它们总体有效性的有价值的讨论。关于评估,我们利用现实物理学模拟器审查传感器对传感器的自我校正的代表性任务。我们还研究这些算法对外感应感应传感器测量信息内容变化的敏感性。