Wei Kun, Ren Bingyin
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2018 Feb 13;18(2):571. doi: 10.3390/s18020571.
In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human-Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator's obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots' path planning.
在未来的智能工厂中，机器人操纵器必须在人机协作和动态非结构化环境中高效且安全地工作。自主路径规划是提高机器人操纵器智能过程中必须首先解决的最重要问题。在路径规划方法中，基于随机采样的快速扩展随机树（RRT）算法已广泛应用于高维机器人操纵器的动态路径规划，特别是在复杂环境中，因为它具有概率完备性、完美扩展性以及相较于其他规划方法更快的探索速度。然而，现有的RRT算法在动态非结构化环境中为机器人操纵器进行路径规划时存在局限性。因此，提出了一种基于改进的RRT算法（称为平滑RRT（S-RRT））的机器人操纵器自主避障动态路径规划方法。该方法针对定向节点进行扩展，可显著提高RRT的采样速度和效率。提出了一种基于最大曲率约束的路径优化策略，为机器人操纵器生成平滑且弯曲的连续可执行路径。最后，通过MATLAB静态仿真、机器人操作系统（ROS）动态仿真环境以及在动态非结构化环境中对机器人操纵器进行的实际自主避障实验，验证了所提方法的正确性、有效性和实用性。所提方法不仅为智能工厂中机器人操纵器的避障提供了重要的实际工程意义，也为其他类型机器人的路径规划提供了理论参考价值。
