Water current is an important factor in the operation of marine robotic vehicles (MRVs). When cruising in a confined area, the perception of the flow field of this area greatly helps MRVs in path planning and improves energy efficiency. Traditional current observations rely on information obtained through buoys and satellites. It is expensive and time-inefficient. Therefore, using the position and velocity information of the vehicle to predict the flow field can significantly improve the time-efficiency and reduce the cost. Motion tomography is a method that uses vehicle navigation information to estimate current-induced flow and generate a flow field map. This method provides a time-efficient and convenient way to monitor water currents. Bio-inspired robotic fish is an ideal agent for shallow water environmental sensing tasks due to its high maneuverability in grassy environments, low noise propulsion, and multi-functional capabilities. Using trajectory of robotic fish to estimate the flow field can significantly benefit transportation and environmental study. To improve the estimation accuracy, we add an active heading control (AHC) to moderate the passive heading change caused by the flow field. With the position and direction data collected from multiple trips, a vectorized flow map could accurately estimate the flow field.

水流是海洋机器人车辆 (MRV) 运行的重要因素。在密闭区域巡航时，对该区域流场的感知极大地帮助MRV进行路径规划，提高能效。传统的当前观测依赖于通过浮标和卫星获得的信息。这是昂贵且时间效率低下的。因此，使用车辆的位置和速度信息来预测流场可以显着提高时间效率并降低成本。运动层析成像是一种利用车辆导航信息来估计电流诱导流并生成流场图的方法。这种方法提供了一种省时且方便的水流监测方法。由于其在草地环境中的高机动性、低噪音推进和多功能能力，仿生机器鱼是浅水环境传感任务的理想代理。使用机器鱼的轨迹来估计流场可以显着有益于交通和环境研究。为了提高估计精度，我们添加了一个主动航向控制（AHC）来缓和由流场引起的被动航向变化。利用从多次行程中收集的位置和方向数据，矢量化流图可以准确地估计流场。我们添加了一个主动航向控制 (AHC) 来缓和由流场引起的被动航向变化。利用从多次行程中收集的位置和方向数据，矢量化流图可以准确地估计流场。我们添加了一个主动航向控制 (AHC) 来缓和由流场引起的被动航向变化。利用从多次行程中收集的位置和方向数据，矢量化流图可以准确地估计流场。