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.

翻译：触觉反馈可在操作员态势感知受限或注意力不集中时提升遥操作机器人的安全性。标准势场法在接近障碍物时会增大触觉阻力——当操作员未察觉障碍物时此特性可取，但在操作员意图性移动（如观察或操控物体）时则不可取。本文提出一种新颖的触觉遥操作框架，通过估计操作员对障碍物的注意力水平来减弱意图性移动场景下的触觉反馈。基于计算工作记忆理论，我们开发了融合视觉显著性估计与空间映射的生物启发式注意力模型。该注意力图可实时生成，系统对操作员已知晓的障碍物施加较低的触觉力。仿真实验结果表明，与未集成注意力估计的触觉遥操作相比，本框架在任务效能、机器人安全性与用户体验方面均表现更优。