Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan.
Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
Sensors (Basel). 2023 May 16;23(10):4814. doi: 10.3390/s23104814.
An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.
本文基于机器人操作系统（ROS）实现了一个带有相机、六自由度（DOF）机器人机械臂和两指夹持器的物体拾取-放置系统。在机器人在复杂环境中自主地拾取-放置物体之前，无碰撞路径规划方法是必须要解决的最基本问题之一。在实时拾取-放置系统的实现中，六自由度机器人机械臂的路径规划成功率和计算时间是两个至关重要的关键因素。因此，提出了一种改进的快速搜索随机树（RRT）算法，称为变更策略 RRT（CS-RRT）。基于基于 RRT 的逐渐改变采样区域的方法（CSA-RRT），所提出的 CS-RRT 采用了两种机制来提高成功率和计算时间。所提出的 CS-RRT 算法采用了采样半径限制机制，每次环境探索时，使随机树更有效地接近目标区域。它可以避免在接近目标点时花费大量时间寻找有效点，从而减少改进的 RRT 算法的计算时间。此外，CS-RRT 算法采用了节点计数机制，使算法在复杂环境中能够切换到适当的采样方法。它可以避免由于在目标点方向上的过度探索而导致搜索路径陷入某些约束区域，从而提高算法对各种环境的适应性并提高成功率。最后，建立了具有四个物体拾取-放置任务的环境，并给出了四个仿真结果，以说明基于所提出的 CS-RRT 的无碰撞路径规划方法与其他两种 RRT 算法相比具有最佳性能。还提供了一个实际实验，以验证机器人机械臂确实可以成功有效地完成指定的四个物体拾取-放置任务。
