Multiple User Intent Prediction Using Interacting Multiple Model Joint Probabilistic Data Association Filter

Abstract

This paper presents a novel method for multi-user motion intent estimation when the motion is observed by a single sensor. A motion model is associated with each of the activities carried out by the operator and the end location of which is termed as a motion intent. Such modeling of intent is useful in human-robot collaborative tasks. The appropriate model selection is achieved via an interacting multiple model (IMM) filter. When the position measurements of multiple users originating from one sensor are close to each other, then the measurement to operator association becomes challenging. A joint probabilistic data association (JPDA) filter is employed to address this issue. The combined IMM and JPDA filter provides a way to infer the motion intent of each operator. Simulation results show that the IMM-JPDA filter tracks two target states reaching toward goal intent in the presence of clutter measurements originating from the Kinect sensor.