Ye Lei, Li Jin, Li Pu
School of Intelligent Engineering, Shaoguan University, Shaoguan, China.
Front Plant Sci. 2024 May 13;15:1337638. doi: 10.3389/fpls.2024.1337638. eCollection 2024.
Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.
对于存在大量不规则障碍物的果园而言，高效的避障路径规划至关重要。本文提出了一种基于双向快速扩展随机树（Bi-RRT）和快速扩展随机树星型算法（Quick-RRT*）的连续双向快速扩展随机树星型算法（CBQ-RRT*），并提出了一种评估路径平滑度和长度的扩展成本函数，以克服Quick-RRT算法在非完整移动机器人应用中的局限性。为了改善由Bi-RRT算法的双树扩展所导致的双树之间的曲折问题，CBQ-RRT提出了创建连接节点优化方法，该方法有效解决了双树交界处的路径平滑度问题。在ROS平台上进行的仿真表明，在各种果园布局和地形条件下，CBQ-RRT在效率方面优于单向Quick-RRT。与Bi-RRT相比，CBQ-RRT的平均路径长度和最大航向角分别降低了8.5%和21.7%。此外，现场测试证实了CBQ-RRT的卓越性能，平均最大路径横向误差为0.334米，相较于Bi-RRT和Quick-RRT有显著改善。这些改进证明了CBQ-RRT在复杂果园环境中的有效性。
