Accelerated Informed RRT*: Fast and Asymptotically Path Planning Method Combined with RRT*-Connect and APF

In recent years, path planning algorithms have played a crucial role in addressing complex navigation problems in various domains, including robotics, autonomous vehicles, and virtual simulations. This abstract introduces a improved path planning algorithm called Informed RRT*-connect based on APF, which combines the strengths of the fast bidirectional rapidly-exploring random tree (RRT-connect) algorithm and the informed RRT* algorithm. The proposed algorithm aims to efficiently find collision-free paths with less iterations and time while minimizing the path length. Unlike traditional RRT-based algorithms, Informed RRT*-connect based on Artificial Potential Fields (APF) incorporates a bidirectional connection and rewiring of a new sampling point to explore the search space. This enables the algorithm to connect both the start and goal nodes more effectively and quickly to find a initial solution, reducing the search time and provide a better initial heuristics sapling for the next optimal steps. Furthermore, Informed RRT*-connect introduces an informed sampling strategy that biases the sampling towards areas of the configuration space likely to yield better paths. This approach significantly reduces the exploration time to find a path and enhances the ability to discover optimal paths efficiently. To evaluate the effectiveness of the Informed RRT*-connect algorithm, we conducted the simulation experiments on two different experiment protocol. The results demonstrate that our approach outperforms existing state-of-the-art algorithms in terms of both planning efficiency and solution optimality.