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Kinematic Constraints Based Bi-directional RRT (KB-RRT) with Parameterized Trajectories for Robot Path Planning in Cluttered Environment

机译:基于运动的基于基于的双向RRT(KB-RRT),具有用于杂乱环境的机器人路径规划参数化轨迹

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Optimal path planning and smooth trajectory planning are critical for effective navigation of mobile robots working towards accomplishing complex missions. For autonomous, real time and extended operations of mobile robots, the navigation capability needs to be executed at the edge. Thus, efficient compute, minimum memory utilization and smooth trajectory are the key parameters that drive the successful operation of autonomous mobile robots. Traditionally, navigation solutions focus on developing robust path planning algorithms which are complex and compute/memory intensive. Bidirectional-RRT(Bi-RRT) based path planning algorithms have gained increased attention due to their effectiveness and computational efficiency in generating feasible paths. However, these algorithms neither optimize memory nor guarantee smooth trajectories. To this end, we propose a kinematically constrained Bi-RRT (KB-RRT) algorithm, which restricts the number of nodes generated without compromising on the accuracy and incorporates kinodynamic constraints for generating smooth trajectories, together resulting in efficient navigation of autonomous mobile robots. The proposed algorithm is tested in a highly cluttered environment on an Ackermannsteering vehicle model with severe kinematic constraints. The experimental results demonstrate that KB-RRT achieves three times (3 X) better performance in terms of convergence rate and memory utilization compared to a standard Bi-RRT algorithm.
机译:最佳路径规划和平滑的轨迹规划对于有效的移动机器人朝向实现复杂任务的有效导航至关重要。对于移动机器人的自主,实时和扩展操作,需要在边缘执行导航功能。因此,有效计算,最小内存利用率和平滑轨迹是驱动自主移动机器人成功运行的关键参数。传统上,导航解决方案专注于开发具有复杂和计算/内存密集型的强大路径规划算法。基于Bidirectional-RRT(Bi-RRT)的路径规划算法由于其有效性和计算效率而产生了可行的路径的有效性和计算效率。但是,这些算法既不优化内存也不保证平滑轨迹。为此,我们提出了一种运动学上受约束的BI-RRT(KB-RRT)算法,其限制了在不损害精度的情况下产生的节点的数量,并结合了用于产生平滑轨迹的kinodynamic约束,共同产生自主移动机器人的有效导航。该算法在具有严重运动约束的Ackermannsteering车辆模型的高度杂乱的环境中进行了测试。实验结果表明,与标准BI-RRT算法相比,KB-RRT在收敛速率和内存利用方面实现了三倍(3×)的性能。

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