Kinetic collision detection: algorithms and experiments

Abstract

Efficient collision detection is important in many robotic tasks, from high-level motion planning in a static environment to low-level reactive behavior in dynamic situations. Specially challenging are problems in which multiple robots are moving among multiple moving obstacles. In this paper we present a number of collision detection algorithms formulated under the kinetic data structures (KDS) framework, a framework for design and analyzing algorithms for objects in motion. The KDS framework leads to event-based algorithms that sample the state of different parts of the system only as often as necessary for the task at hand. Earlier work has demonstrated the theoretical efficiency of KDS algorithms. In this paper we present new algorithms and demonstrate their practical efficiency as well as by an implementable and direct comparison with classical broad and narrow phase collision detection techniques.