In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city's canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback–Leibler control cost to measure the deviation of the autonomous vessel's movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

中文翻译：

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城市自主水面舰艇的社会航迹规划

在本文中，我们提出了一种轨迹规划算法，该算法可使自主水面舰艇执行 符合社会要求在城市的运河中航行。提出的算法背后的关键思想是采用一种最优控制公式，其中自主船的运动与人为操作的船的名义运动之间的偏差将受到惩罚。因此，给定一对起点和终点，它会发现类似于人类操纵船只的船只轨迹。为此，我们采用核密度估计（KDE），从预先记录的轨迹数据集中建立人为操作的船只的名义运动模型，并使用Kullback-Leibler控制成本来衡量自主船只的运动与模型的偏差。我们在轨迹规划方法和最大熵逆强化学习（MaxEntIRL）方法之间建立类比，以说明我们的方法可以学人工船只的航行行为。另一方面，我们将我们的方法与MaxEntIRL方法区分开来，因为它不需要定义明确的基础（通常称为特征）来构建其成本函数，这是轨迹规划环境中许多逆向强化学习方法所要求的。通过使用从自动识别系统收集的船只轨迹数据集进行的实验，我们证明了我们的方法生成的轨迹类似于人工操作的船只，并且将其用于运河航行有利于减少船只之间的正面碰撞并改善航行安全。