Reactive motion planning with qualitative constraints

Abstract

Qualitative modeling tends to be closer to human type of reasoning than traditional numerical modeling and proved to be very useful in certain branches of cognitive robotics. However, due to the lack of precise numerical relations, planning with qualitative models has been achieved to a limited extent. Typically, it is bound to predicting possible future behaviors of the system, and demands additional exploration of numerical relations, before constructed plans can be executed. In this paper we show how qualitative models can be interpreted in terms of reactive planning, to produce executable actions without the need for additional numerical learning. We demonstrate our method on two classical motion planning problems – pursuing and obstacle avoidance, and a complex problem of pushing objects.