Input Estimation for Teleoperation : Using Minimum Jerk Human Motion Models to Improve Telerobotic Performance

Abstract: This thesis treats the subject of applying human motion models to create estimators for the input signals of human operators controlling a telerobotic system.In telerobotic systems, the control signal input by the operator is often treated as a known quantity. However, there are instances where this is not the case. For example, a well-studied problem is teleoperation under time delay, where the robot at the remote site does not have access to current operator input due to time delays in the communication channel. Another is where the hardware sensors in the input device have low accuracy. Both these cases are studied in this thesis. A solution to these types of problems is to apply an estimator to the input signal. There exist several models that describe human hand motion, and these can be used to create a model-based estimator. In the present work, we propose the use of the minimum jerk (MJ) model. This choice of model is based mainly on the simplicity of the MJ model, which can be described as a fifth degree polynomial in the cartesian space of the position of the subject's hand. Estimators incorporating the MJ model are implemented and inserted into control systems for a teleoperatedrobot arm. We perform experiments where we show that these estimators can be used for predictors increasing task performance in the presence of time delays. We also show how similar estimators can be used to implement direct position control using a handheld device equipped only with accelerometers.