Wang Lina, Yang Xin, Chen Zeling, Wang Binrui
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China.
Biomimetics (Basel). 2023 Aug 17;8(4):374. doi: 10.3390/biomimetics8040374.
When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.
当智能移动机器人在复杂狭窄环境中进行全局路径规划时，常常会出现几个问题，包括搜索效率低、节点冗余、路径不光滑以及成本高。本文提出了一种基于快速扩展随机树（RRT）方法的改进路径规划算法。首先，采用目标偏差采样方法筛选并消除冗余采样点。其次，引入自适应步长策略以解决传统RRT算法的局限性。然后对移动机器人进行建模与分析，以确保路径在移动过程中符合角度和碰撞约束。最后，对初始路径进行修剪，并使用三次B样条曲线对路径进行平滑处理，从而得到成本降低且更光滑的路径。所采用的评估指标包括搜索时间、路径长度和采样节点数量。为了评估所提算法的有效性，在各种环境中对RRT算法、RRT-connect算法、RRT算法和改进的RRT算法进行了仿真。结果表明，改进的RRT算法与RRT算法相比，生成路径长度减少了25.32%，与RRT-connect算法相比减少了26.42%，与RRT算法相比减少了4.99%。此外，改进的RRT算法显著提高了降低路径成本的需求。改进的RRT算法的规划时间与RRT算法相比减少了64.96%，与RRT-connect算法相比减少了40.83%，与RRT*算法相比减少了27.34%，从而提高了速度。这些结果表明，所提方法在采样时间、节点数量和路径长度这三个关键评估指标上有显著改进。此外，在具有狭窄电梯入口的真实环境中，使用类昆虫移动机器人进行物理验证后，该算法表现良好。
