Inspired by the phototropism of plants, a novel variant of the rapidly exploring random tree algorithm as called phototropism rapidly exploring random tree is proposed. The phototropism rapidly exploring random tree algorithm expands less tree nodes during the exploration period and has shorter path length than the original rapidly exploring random tree algorithm. In the algorithm, a virtual light source is set up at the goal point, and a light beam propagation method is adopted on the map to generate a map of light intensity distribution. The phototropism rapidly exploring random tree expands its node toward the space where the light intensity is higher, while the original rapidly exploring random tree expands its node based on the uniform sampling strategy. The performance of the phototropism rapidly exploring random tree is tested in three scenarios which include the simulation environment and the real-world environment. The experimental results show that the proposed phototropism rapidly exploring random tree algorithm has a higher sampling efficiency than the original rapidly exploring random tree, and the path length is close to the optimal solution of the rapidly exploring random tree* algorithm without considering the non-holonomic constraint.

中文翻译：

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向光性快速探索随机树：一种基于植物向光性的高效快速探索随机树方法

受植物向光性的启发，提出了快速探索随机树算法的一种新变体，称为向光性快速探索随机树。与原快速探索随机树算法相比，向光性快速探索随机树算法在探索期间扩展的树节点更少，路径长度更短。该算法在目标点设置虚拟光源，在地图上采用光束传播的方法生成光强分布图。向光性快速探索随机树将其节点向光照强度较高的空间扩展，而原始快速探索随机树基于均匀采样策略扩展其节点。向光快速探索随机树的性能在包括模拟环境和现实世界环境的三种场景中进行了测试。实验结果表明，所提出的向光性快速探索随机树算法比原快速探索随机树算法具有更高的采样效率，且路径长度接近快速探索随机树*算法在不考虑非完整的情况下的最优解。约束。