实时调整机械臂轨迹，精准抓取不规则物体
Online Trajectory Replanner for Dynamically Grasping Irregular Objects
- 分两阶段规划：10秒离线生成初始路径，100毫秒在线重规划
- 在真实场景中实现厘米级抓取精度，有效补偿视觉误差
- 适合动态抓取任务，尤其对不规则物体有强适应性
本文提出一种针对不规则物体的在线轨迹重规划方法。与传统假设物体几何简单的抓取任务不同，本方法旨在实现动态抓取，需在抓取过程中持续调整。为此，我们设计了一个包含两个阶段的轨迹优化框架：首先，在10秒内离线计算从机器人初始状态到抓取目标并运送至预设位置的初始轨迹；其次，通过每100毫秒内完成的快速在线轨迹优化，实时更新运动路径，以缓解视觉系统带来的位姿估计误差。为应对模型偏差、外部扰动及其他未建模影响，我们还为机器人和夹爪分别设计了轨迹跟踪控制器，以执行该框架产生的最优轨迹。大量仿真与真实场景实验结果充分验证了所提框架在动态抓取任务中的有效性。
原文摘要 · Abstract (English)
This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a "dynamic grasp" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular objects, we propose a trajectory optimization framework that comprises two phases. Firstly, in a specified time limit of 10s, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-defined target location. Secondly, fast online trajectory optimization is implemented to update robot trajectories in real-time within 100 ms. This helps to mitigate pose estimation errors from the vision system. To account for model inaccuracies, disturbances, and other non-modeled effects, trajectory tracking controllers for both the robot and the gripper are implemented to execute the optimal trajectories from the proposed framework. The intensive experimental results effectively demonstrate the performance of our trajectory planning framework in both simulation and real-world scenarios.
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