Abstract
The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.
Highlights
Mobile robots represented by autonomous vehicles have been widely used in transportation, agriculture, and industry [1]. e autonomous vehicle consists of perception, decision-making, and control system, and the study of robot planning system has always been a core problem
Based on the above analysis of vehicle path planning problem, this paper proposes a set of path planning algorithms suitable for autonomous vehicles based on the RRT framework. e steps of the algorithm are shown as the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) algorithm. ere are two main improvements in the algorithm, as Figure 7 shows
We conduct a real experiment on the autonomous vehicle for the purpose of verifying the utilities of the path planning method
Summary
Mobile robots represented by autonomous vehicles have been widely used in transportation, agriculture, and industry [1]. e autonomous vehicle consists of perception, decision-making, and control system, and the study of robot planning system has always been a core problem. Mobile robots represented by autonomous vehicles have been widely used in transportation, agriculture, and industry [1]. E autonomous vehicle consists of perception, decision-making, and control system, and the study of robot planning system has always been a core problem. In the situations of complex dynamic environments, the intelligence and adaption of these path planning methods need to be enhanced [6]. When searching path based on graph, the ability of the search algorithms that are widely used has been shown in mobile robot path planning. Is algorithm can search rapidly, but when the environment is complex, this algorithm cannot be widely used in the narrow passages. D∗ algorithm has been proposed with the aim of navigating autonomous vehicles in the environment of 2D, and the main advantage of D∗ method is that it can find an optimal
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