Explore the vast range of titles available.

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.

Aiming at the problems of blind search, low environmental adaptability, and long path that exist in the Informed RRT* algorithm for AUV underwater 3D path planning, this paper introduces an improved version of the Informed RRT* algorithm. This enhanced algorithm introduces an adaptive step size to enhance the applicability and reduce the iteration count. Additionally, a bidirectional extension strategy is employed in the initial phase to minimize the time required for searching the initial path, and an angle constraint is added to improve goal-point orientation and search efficiency. A goal-directed sampling strategy is incorporated to further increase the efficiency of tree growth toward the goal point. Furthermore, a pruning strategy is used to optimize the found path by shortening the path length and reducing the number of path nodes, thus enhancing the smoothness of the path found by the algorithm. In this study, the underwater 3D environment modeling considers both the seabed topography and ocean currents, with ocean current information extracted and interpolated. The path length and the influence of ocean currents are taken as indicators to evaluate the path in the objective function. Simulation results demonstrate that the improved Informed RRT* algorithm provides a more effective solution for AUV underwater 3D path planning.

Efficient and effective path planning can significantly enhance the task execution capabilities of UAVs in complex environments. This paper proposes an improved sampling-based path planning algorithm, Bi-APF-RRT*, which integrates an Artificial Potential Field (APF) method with a newly introduced repulsive coefficient and incorporates dynamic step size adjustments. To further improve path planning performance, the algorithm introduces strategies such as dynamic goal biasing, target switching, and region-based adaptive sampling probability. The improved Bi-APF-RRT* algorithm effectively controls sampling direction and spatial distribution during the path search process, avoiding local optima and significantly improving the success rate and quality of path planning. To validate the performance of the algorithm, this paper conducts a comparative analysis of Bi-APF-RRT* against traditional RRT* in multiple simulation experiments. Quantitative results demonstrate that Bi-APF-RRT* achieves a 59.6% reduction in average computational time (from 5.97 s to 2.41 s), a 20.6% shorter path length (from 691.56 to 549.21), and a lower average path angle (reduced from 33.28° to 29.53°), while maintaining a 100% success rate compared to 95% for RRT*. Additionally, Bi-APF-RRT* reduces the average number of nodes in the search tree by 45.8% (from 381.17 to 206.5), showcasing stronger obstacle avoidance capabilities, faster convergence, and smoother path generation in complex 3D environments. The results highlight the algorithm’s robust adaptability and reliability in UAV path planning.