Design of a Kinesthetic Perception-Based Sampler for Haptic Application

Kinesthetic and cutaneous cues are essential for humans to perform dexterous manipulation tasks in daily activities. This research focuses on perception based sampler for enhanced human-robot interaction for haptic based applications. The work presents an end-to-end haptic application with a perception-based adaptive sampling. The application entails a scenario where an operator controls a dexterous robotic gripper, which acts as User Equipment (UE). A custom designed gripper acts as a UE to manipulate a real-world object. During interaction, the contact force (tactile data) from the gripper is rendered as kinesthetic feedback through a haptic device. For kinesthetic-based sampler design, psychophysics-based experiments are conducted to identify the Temporal, spatial deadzone and to study the influence of varying stimulus impacts on kinesthetic perception in the context of perceptually adaptive sampling for teleoperation. The key contributions of this work include the development of spatial and temporal deadzones for tactile-assisted dexterous tasks, the design and comparative analysis of perception-driven sampling methods, and the implementation of a human-in-the-loop control system that enhances the manipulation of virtual objects. Additionally, the application is validated by mapping it onto a 3GPP EDGEAPP-based testbed.