A Path Planning Algorithm based on Leading Rapidly-exploring Random Trees

Nowadays, unmanned vehicles for navigation applications, such as factory unmanned vehicles and delivery dining systems, are becoming more and more extensive and gradually playing an indispensable role in our lives. Basically, navigation can be divided into three fields, the mapping, the localization and the path planning, in which the path planning is to use a pre-built map to plan a feasible path given the starting and the ending points. Hence, the path planning is the core part of the navigation operation, which is very important at the robot application level, such as the automatic driving and the driverless driving. For aircraft and space exploration, the path planning algorithms can be roughly divided into two types, the graph-based searching and the sampling-based searching. Among the two, the path planning based on random sampling provides with fast operation speed, high success rate on high-dimensional and complex problems, and disuse of extra considerations. The constraint of non-holonomic constraints, and the fast search for random trees are an algorithm based on random sampling. This paper mainly focuses on improving the path divergence in fixed iteration and on leading the direction from root to goal in fixed iteration condition. From the comparison of the experimental results, our approach is approximately 1.5 times better than the RRT on average path length.