Background Among the existing path planning algorithms, the search-based A* algorithm is highly dependent on grid maps and incurs large computational memory overhead at high resolutions. Meanwhile, the sampling-based RRT algorithm and RRT* algorithm suffer from low search efficiency and long path planning time.

Purpose Aiming at the shortcomings of existing algorithms and the requirement for real-time path planning, a brand-new hybrid algorithm strategy is proposed.

Methods Firstly, based on the A* algorithm’s dependence on grid maps, an omnidirectional optional path search perspective is put forward. Secondly, in view of the strong randomness and low search efficiency of sampling-based algorithms, a global optimal path search direction and a new efficient node selection method are proposed. Near obstacles, the idea of a simple and convenient wall-following algorithm is adopted to avoid falling into local minima while further shortening the running time of path planning.

Results Simulation results show that in obstacle environments of different complexities, compared with the traditional A* algorithm, RRT algorithm, and RRT* algorithm, the new hybrid algorithm reduces the path planning time by more than 96%, shortens the generated path length to varying degrees, and decreases the average number of iterations by more than 98%.

Conclusions The Mixed algorithm demonstrates excellent adaptability in three typical environments: it can rapidly generate near-optimal paths in open and regular environments; it also achieves high planning efficiency in cluttered but wide-passage environments; and it can efficiently find short paths even in challenging environments with narrow passages. Comparisons with the A*, RRT, and RRT* algorithms further verify that the Mixed algorithm performs outstandingly in terms of both path quality and search efficiency.Therefore, this algorithm can significantly improve the efficiency of path planning and provide strong support for the efficient autonomous path planning of UAV obstacle avoidance.