Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.
翻译:以抽样为基础的路径规划算法通常采用统一的取样方法搜索国家空间。然而,统一的取样方法可能导致许多情况不必要地进行探索,如环境等环境有几条死胡同。我们先前的工作提议利用有希望的区域来指导取样进程解决这一问题。然而,预计有希望的区域往往相互脱节,这意味着它们无法将起点和目标状态联系起来,从而导致缺乏概率完整性。这项工作的重点是加强预测有希望区域的连接性。我们提议的方法会减少x和Y方向边缘的连接性概率。此外,它计算了有希望的损失边缘的重量,以指导神经网络更多地关注有希望区域的连通性。我们进行了一系列模拟实验,结果显示有希望的区域的连通性显著改善。此外,我们分析了连接性对基于取样路径的规划算法的影响,并得出结论,连接性在维持算法性方面起着至关重要的作用。