  Challenges persist in nonholonomic robot navigation in dynamic environments.
This paper presents a framework for such navigation based on the model of
generalized velocity obstacles (GVO). The idea of velocity obstacles has been
well studied and developed for obstacle avoidance since being proposed in 1998.
Though it has been proved to be successful, most studies have assumed equations
of motion to be linear, which limits their application to holonomic robots. In
addition, more attention has been paid to the immediate reaction of robots,
while advance planning has been neglected. By applying the GVO model to
differential drive robots and by combining it with RRT*, we reduce the
uncertainty of the robot trajectory, thus further reducing the range of
concern, and save both computation time and running time. By introducing
uncertainty for the dynamic obstacles with a Kalman filter, we dilute the risk
of considering the obstacles as uniformly moving along a straight line and
guarantee the safety. Special concern is given to path generation, including
curvature check, making the generated path feasible for nonholonomic robots. We
experimentally demonstrate the feasibility of the framework.
