Ran Kemeng, Wang Yujun, Fang Can, Chai Qisen, Dong Xingxiang, Liu Guohui
College of Computer and Information Science, Southwest University, Chongqing 400715, China.
Sensors (Basel). 2024 Dec 6;24(23):7812. doi: 10.3390/s24237812.
In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.
在本文中，我们提出了一种基于快速扩展随机树*（RRT*）算法的算法，用于运动学约束下移动机器人的路径规划，旨在快速生成高效且平滑的路径。与其他算法相比，我们提出的算法的主要贡献如下：第一，我们引入了一种双向扩展策略，该策略能在短时间内快速识别出通往目标点的直接路径。第二，使用节点重新连接策略来消除不必要的节点，从而缩短路径长度并节省内存。第三，设计了一种基于回旋曲线的路径变形策略，以增强避障和路径规划能力，确保符合移动机器人运动学约束的无碰撞路径。仿真结果表明，与现有算法相比，我们的算法更简单、计算效率更高、加快了路径查找速度、实现了更高的成功率并生成了更平滑的路径。
