移动航标的产生与发展对海上科技进步具有正面的推动作用，自主浮标作为移动航标的一种，有着很大的发展前景。但是，由于能源限制，浮标在航行过程中必须尽可能减少能量消耗，以延长其工作周期。所以，需要一款可以更加节能的路径规划算法。研究提出了一种能量感知型A*与人工势场混合算法，利用能量感知型A*算法进行自主浮标的全局路径规划，在传统A*算法的基础上增加能量感知能力，建立能量消耗模型，规划出耗能更低的路线，并引入时间成本项，控制自主浮标航行耗时。加入人工势场算法进行局部运动规划，对自主浮标的运动路径进行局部控制，避免碰撞。通过仿真验证了能量感知型A*算法的优越性，并通过引入人工势场法，形成混合规划算法，实现了自主浮标实时避碰规划。现有的其他A*-APF混合算法，大多是为了机器人、无人船等而设计，不适合能量局限性强的自主浮标的实际路径规划，相较而言，本混合算法强调能量感知能力，更适合应用于自主浮标的实际路径规划。本算法在自主浮标设计中的应用可以推动自主浮标应用的发展，对增强海上科技力量具有正面意义。

The emergence and development of mobile aids to navigation （MAtoN） have a promoting effect on the progress of maritime science and technology. As a type of MAtoN， autonomous buoys have great development prospects. However， due to energy constraints， autonomous buoys must minimize energy consumption as much as possible during navigation to extend their working cycle. Therefore， a path planning algorithm that can be more energy-efficient is needed. This paper proposes a hybrid algorithm of energy-aware A* and artificial potential field （APF）. The global path planning of autonomous buoys is carried out by using the energy-aware A* algorithm. Based on the traditional A* algorithm， the energy perception ability is added， an energy consumption model is established， then a route with lower energy consumption is planned， and a time costiten is introduced to control the navigation time of the autonomous buoy. The APF algorithm is added for local motion planning to locally control the motion path of the autonomous buoy and avoid collisions. This paper verifies the superiority of the energy-aware A* algorithm through simulation. By introducing the APF algorithm， a hybrid planning algorithm is formed， achieving real-time collision avoidance planning for autonomous buoys. Most of the existing A*-APF hybrid algorithms are designed for robots， unmanned surface vehicles， etc.， and are not suitable for the actual path planning of autonomous buoys with strong energy limitations. In contrast， this hybrid algorithm emphasizes energy-aware ability and is more suitable for application in the actual path planning of autonomous buoys. The application of this algorithm in the design of autonomous buoys can promote the development of autonomous buoy applications and has positive significance for enhancing maritime scientific and technological strength.