Obstacle avoidance in dynamic environments based on velocity space optimization

Abstract

Robotic obstacle avoidance is an important issue in robotic navigation for unknown or partially known, dynamic environments. A good number of techniques have already been proposed to navigate obstacles in this kind of environment. They include a series of velocity space methods that have been successful implemented in several applications. They formulate the problem as one of constrained optimization in the velocity space of the robot. The constraints include the obstacles in the environment assuming they are static. In this paper, we present an efficient, real-time method (BCM-DO) to include the restrictions imposed by dynamic objects. The optimization function has also been adapted to include these new restrictions. The new function is evaluated in two sets of points. A first set is obtained from a coarse sampling in the reachable window of velocities and a second set is selected in the limits of each curvature interval to avoid missing small openings between static objects. The whole system has first been extensively tested in several simulated robots and finally applied to a hotel assistant robot (BellBot) resulting in an efficient, real-time obstacle avoidance method that produces smooth and reliable routes.