Abstract
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.
Z. Tu and W. Zhuang—Contribute equally to this work. This work was supported in part by the National Natural Science Foundation of China [Grant U1913205, 52175272], in part by the Science, Technology, and Innovation Commission of Shenzhen Municipality [Grant: ZDSYS20200811143601004, JCYJ20220530114809021], and in part by the Stable Support Plan Program of Shenzhen Natural Science Fund under Grant 20200925174640002.
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Tu, Z., Zhuang, W., Leng, Y., Fu, C. (2023). Accelerated Informed RRT*: Fast and Asymptotically Path Planning Method Combined with RRT*-Connect and APF. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14274. Springer, Singapore. https://doi.org/10.1007/978-981-99-6501-4_24
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