论文标题
通过多目标轨迹优化的预期人类机器人协作
Anticipatory Human-Robot Collaboration via Multi-Objective Trajectory Optimization
论文作者
论文摘要
我们解决了适应机器人轨迹以提高人机协作任务的安全性，舒适性和效率的问题。为此，我们提出了Comoto，这是一种轨迹优化框架，利用随机运动预测模型预测人类的运动并相应地适应机器人的关节轨迹。我们设计了一个多目标成本函数，该功能同时优化了i）分离距离，ii）最终效果的可见性，iii）易读性，iv）效率和v）平滑度。当与人类近距离时，我们针对机器人轨迹产生的三种现有方法评估了孔野。我们的实验结果表明，我们的方法始终优于一组安全性，舒适性和效率指标的现有方法。
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in human-robot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction models to anticipate the human's motion and adapt the robot's joint trajectory accordingly. We design a multi-objective cost function that simultaneously optimizes for i) separation distance, ii) visibility of the end-effector, iii) legibility, iv) efficiency, and v) smoothness. We evaluate CoMOTO against three existing methods for robot trajectory generation when in close proximity to humans. Our experimental results indicate that our approach consistently outperforms existing methods over a combined set of safety, comfort, and efficiency metrics.
