Abstract: A safer and more efficient inspection of water quality is highly required in an aquatic-air amphibious robot. However, the traditional inspection mode of water quality cannot fully arrive at multiple target points over a pond. It is very necessary to plan a reasonable path for the robot, in order to reduce the dwelling time with cost saving. Particularly, the traditional path zigzagging of A* algorithm cannot consider the different constraints of multi-amphibious robots. In this study, an improved A* algorithm of path planning was proposed for the high search efficiency in the water-air amphibious robots. Firstly, the distribution and height information of the obstacles was collected using the improved A* algorithm. A 2.5-dimensional raster map was then established to contain the multiple water areas; Secondly, the evaluation function of A* algorithm was used to add the different energy consumption, time, and safety costs of amphibious robots. The different weights were then adjusted to obtain the initial paths; The weights among energy consumption, time, and safety costs were also dynamically allocated to improve the path planning of A* algorithm. The heuristic function of A* algorithm was then improved to speed up the search efficiency. The target cost function was used to prioritize all detection targets of water quality, in order to realize the path planning of multiple target points in a partitioned fishpond; Finally, the initial paths were optimized to add the additional airborne mode-switching, thus deleting the redundant points. The B-spline algorithm was adopted to generate a three-dimensional smooth trajectory. As such, an optimal path was obtained to connect the multiple inspection points of water quality. Simulation results show that the number of iterations of the improved A* algorithm was reduced by 70.04% and 68.07%, respectively, compared with the traditional and land-air A* algorithms; while the length of the path was reduced by 35.44% and 7.6%, respectively; the total angle of turn was reduced by 83.63% and 8.65%, respectively; and the number of dangerous nodes was reduced by 80.67% and 33.33%, respectively. The real water test showed that the number of iterations of the improved A* algorithm was reduced by 84.89% and 83.78%, respectively; the length of the path was reduced by 12% and 0.6%, respectively; the total angle of turn was reduced by 73.21% and 22.1%, respectively; and the number of dangerous nodes was reduced by 84.62% and 80%, respectively. The improved A* algorithm was achieved in the safe and smooth path, according to the multiple target points in multiple pieces of water. The efficiency of water quality detection was effectively improved in the partitioned fishponds, compared with the traditional approach. The finding can also provide a strong reference for the autonomous navigation of multi-habitat robots.