用双参考系LQR实现摇晃苹果的高效无损抓取
DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration
- 双LQR在移动参考系中协同控制，无需重算
- 摇晃环境下仍达99%成功率，路径最短
- 适合农业机器人抓取任务，实测验证有效
示范学习为机器人执行农业任务（如选择性采摘）提供了巨大潜力。其中一大挑战是目标果实接近时处于振荡状态。抓取振荡目标需满足两个要求：1）末端接近阶段紧密跟踪目标以避免损伤；2）全程路径尽可能短以提升效率。本文提出一种名为DualLQR的新方法：在不重新拟合LQR的前提下，对运动目标使用有限时域线性二次调节器（LQR）。为此，采用双LQR架构，分别在两个独立参考系中运行。大量仿真测试表明，现有最优方法在无振荡时勉强达标，但在有振荡时精度下降；而DualLQR即使在高振荡条件下也能保持所需精度，且路径最短。进一步在真实苹果抓取任务中的测试显示，DualLQR成功抓取振荡苹果，成功率高达99%。
原文摘要 · Abstract (English)
Learning from Demonstration offers great potential for robots to learn to perform agricultural tasks, specifically selective harvesting. One of the challenges is that the target fruit can be oscillating while approaching. Grasping oscillating targets has two requirements: 1) close tracking of the target during the final approach for damage-free grasping, and 2) the complete path should be as short as possible for improved efficiency. We propose a new method called DualLQR. In this method, we use a finite horizon Linear Quadratic Regulator (LQR) on a moving target, without the need of refitting the LQR. To make this possible, we use a dual LQR set-up, with an LQR running in two separate reference frames. Through extensive simulation testing, it was found that the state-of-art method barely meets the required final accuracy without oscillations and drops below the required accuracy with an oscillating target. DualLQR, on the other hand, was found to be able to meet the required final accuracy even with high oscillations, while travelling the least distance. Further testing on a real-world apple grasping task showed that DualLQR was able to successfully grasp oscillating apples, with a success rate of 99%.
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