  The navigation of robots in dynamic urban environments, requires elaborated
anticipative strategies for the robot to avoid collisions with dynamic objects,
like bicycles or pedestrians, and to be human aware. We have developed and
analyzed three anticipative strategies in motion planning taking into account
the future motion of the mobile objects that can move up to 18 km/h. First, we
have used our hybrid policy resulting from a Deep Deterministic Policy Gradient
(DDPG) training and the Social Force Model (SFM), and we have tested it in
simulation in four complex map scenarios with many pedestrians. Second, we have
used these anticipative strategies in real-life experiments using the hybrid
motion planning method and the ROS Navigation Stack with Dynamic Windows
Approach (NS-DWA). The results in simulations and real-life experiments show
very good results in open environments and also in mixed scenarios with narrow
spaces.
