Steering a Tractor by Means of an EMG-Based Human-Machine Interface

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering

DETC2009-86199 ELLIPSOIDAL APPROXIMATIONS OF INVARIANT SETS IN STABILIZATION PROBLEM FOR A WHEELED ROBOT FOLLOWING A CURVILINEAR PATH

ABSTRACT A stabilization problem for a wheeled robot following a curvilinear target path is studied. In [1], a method for constructing invariant ellipsoids-quadratic approximations of the attraction domains for the target trajectory under a given control lawwas developed. A basic result of that study is a theorem by means of which construction of the invariant ellipsoids reduces to solving a system of linear matrix inequalities (LMIs) and checking a scalar inequality. This paper is a sequel to work [1] and is devoted to practical implementation of the results obtained in that paper. It is discussed how to select the parameters in terms of which the theorem is formulated. An algorithm is developed that, for a given value of maximal deviation from the target trajectory, constructs an invariant ellipsoid of as large volume as possible