RDT-RRT: Real-time double-tree rapidly-exploring random tree path planning for autonomous vehicles

Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm

“…As previously discussed, the optimal kinodynamic motion planning using incremental sampling-based methods (RRT*) [30] algorithm has emerged as the leading approach for path planning in robotics, owing to its superior performance and other state-of-the-art methodologies. Consequently, recent research endeavors have focused on leveraging the RRT* methodology to expedite the convergence and search processes.…”

“…Figure 1. Schematic diagram of optimal kinodynamic motion planning using incremental samplingbased methods (RRT*) [30] . The meaning of X init is initial position.…”

Recent advances in Rapidly-exploring random tree: A review

“…Aiming at the targeted dynamic threat (radar or missile tracking) and random dynamic threat (tracking moment and tracking speed) that UAV may face, Guo et al [ 151 ] propose a time-based sampling process for continuous change process of dynamic obstacles, fuse the APF structure for potential collision process, and introduce the cost function consisting of the true distance cost and the estimated distance cost to construct the heuristic path-finding process, and the comparative experiments verify the advantages of this algorithm in terms of the navigation time, path length, and the success rate of generated paths. Yu et al [ 195 ] construct a miniature circular runway as an autopilot environment for a car-like robot and set moving obstacles across the runway as dynamic obstacles, and the algorithm can plan the trajectory points over time in advance according to the trajectories of the moving obstacles and smooth the paths under the premise of satisfying the curvature constraints of the car-like robot, tracking the trajectories at a speed of 0.17 m/s and avoiding the obstacles successfully. For the robotic arm path planning problem in a dynamic scene, Yuan et al [ 161 ] compare D-RRT and DBG-RRT, and both algorithms have good real-time performance and high success rate of obstacle avoidance under three different maps.…”