论文标题
专心图估算移动机器人障碍物避免和方法的触觉远程操作
Attentiveness Map Estimation for Haptic Teleoperation of Mobile Robot Obstacle Avoidance and Approach
论文作者
论文摘要
当情况意识有限或操作员不专心时，触觉反馈可以提高远程手工机器人的安全性。标准潜在的野外接近会增加触觉阻力，因为当操作员不了解障碍物时，这是可取的，但是当运动是故意的，例如操作员希望检查或操纵物体时，这是不受欢迎的。本文提出了一个新颖的触觉远程流动框架，该框架估计了操作员对障碍的关注，并抑制了故意运动的触觉反馈。生物学启发的注意模型是基于计算工作记忆理论开发的，以将视觉显着性估计与空间映射整合在一起。专注力图是实时生成的，我们的系统使较低的触觉力估计要意识到操作员的障碍。模拟中的实验结果表明，所提出的框架在任务绩效，机器人安全性和用户体验方面没有专心估算的触觉远程操作。
Haptic feedback can improve safety of teleoperated robots when situational awareness is limited or operators are inattentive. Standard potential field approaches increase haptic resistance as an obstacle is approached, which is desirable when the operator is unaware of the obstacle but undesirable when the movement is intentional, such as when the operator wishes to inspect or manipulate an object. This paper presents a novel haptic teleoperation framework that estimates the operator's attentiveness to obstacles and dampens haptic feedback for intentional movement. A biologically-inspired attention model is developed based on computational working memory theories to integrate visual saliency estimation with spatial mapping. The attentiveness map is generated in real-time, and our system renders lower haptic forces for obstacles that the operator is estimated to be aware of. Experimental results in simulation show that the proposed framework outperforms haptic teleoperation without attentiveness estimation in terms of task performance, robot safety, and user experience.
