Identification of haptic based guidance in low visibility conditions

Student thesis: Doctoral ThesisDoctor of Philosophy


This thesis presents identification of abstracted dynamics of haptic based human control policies and human responses in guiding/following using hard reins in low visibility conditions. The extracted haptic based guidance policies can be implemented on a robot to guide a human in low visibility conditions like in indoor fire-fighting, disaster response, and search and rescue.
Firstly, the thesis presents haptic based guidance in Human-human interactions. The control policies were modeled by a simple linear Auto-Regressive model (AR). It was found that the guiding agent’s control policy can be modeled as a 3rd order predictive AR system and the human follower can be modeled as a 2nd order reactive AR system. Secondly, the human follower’s dynamics were modeled by a time varying virtual damped inertial system to understand how trust in the guider is reflected by physical variables. Experimental
results on human trust showed that the coefficient of virtual damping is most sensitive to the follower’s trust. Thirdly, the thesis evaluates human-robot interactions when the control policy identified from human guiders was implemented on a planar 1-DoF robotic arm to perturb the blindfolded
subjects’ most dominant arm to guide them to a desired position in leftward/rightward directions. Experiments were carried with naive and trained subjects. Humans’ behavior in leftward/rightward movements are asymmetric for naive subjects and symmetric for trained subjects. Moreover, it was found that naive subjects elicit a 2nd order reactive behavior similar to human demonstration experiments. However, trained subjects developed a 2nd order predictive following behavior. Furthermore, naive and trained subjects’ arm muscle activation is significantly different in leftward/rightward arm perturbation. Finally, the thesis presents how humans trained in primitive haptic patterns given using a wearable sleeve, can recognize their shifts and linear combinations.
Date of Award2015
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorThrishantha Nanayakkara (Supervisor) & Kaspar Althoefer (Supervisor)

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