Thrishantha Nanayakkara

This paper proposes a multi-fingered palpation method which employs pneumatic haptic feedback actu- ators allowing users to experience haptic sensations at multiple fingers while carrying out remote soft tissue palpation. Pneumatic actuators are used to vary the stress on the user’s fingertips in accordance with the tissue stiffness, experienced during manual palpation. The proposed method reduces actuator elements compared to tactile actuators and provides more information than single-point… Show more

This paper proposes a multi-fingered palpation method which employs pneumatic haptic feedback actu- ators allowing users to experience haptic sensations at multiple fingers while carrying out remote soft tissue palpation. Pneumatic actuators are used to vary the stress on the user’s fingertips in accordance with the tissue stiffness, experienced during manual palpation. The proposed method reduces actuator elements compared to tactile actuators and provides more information than single-point force feedback. The results of our finite element analysis have proven that our pneumatic haptic feedback device can recreate the contact stress between fingertip and soft tissue during palpation. The accuracy (96.8% vs. 93.3%) and time-efficiency (4.6 s vs. 8.3 s) advantages of using three-fingered over single-fingered pal- pation have been confirmed in our user study results of stiffness levels discrimination. Relatively good tumor detection sensitivities have been demonstrated by the palpation user study which has showed a direct correlation between tumor size and detection sensitivity and has further proven the efficiency of the proposed actuator and multi-fingered palpation method for tumor detection in palpation simulation. Show less

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance… Show more

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance parameters of the soft object is assumed to undergo Gaussian random variations. Here we demonstrate that the proposed model free grip controller can maintain a safe grip at two diagonally opposite points of the object merely based on the statistics of the normal force. It accomplishes this by computing a probability of grip failure to adapt the compression on the soft object. A novel optimal estimation algorithm that can concurrently estimate the unknown impedance parameters of the object and the states of the coupled dynamic system is discussed as a potential tool to be used in predictive optimal impedance control on uncertain objects. Experimental results on adaptive grip control on a cylindrical tube inflated and deflated with a Gaussian random variation has been presented to validate the algorithm. Show less