Cameras provide valuable information about the environment and are
often used as sensor for localization to accomplish navigation
tasks. However, fast movements of a mobile robot typically reduce
the performance of vision-based localization systems due to motion
blur. We developed a reinforcement learning approach to select
appropriate velocity values for vision-based navigation. The
learned policy minimizes the time to reach the destination and
implicitly takes the impact of motion blur on observations into
account. To reduce the size of the resulting policies, which is
desirable in the context of memory-constrained systems, we compress
the learned policy via a clustering approach. Extensive simulated
and real-world experiments demonstrate that our learned policy
significantly outperforms any policy that uses a constant velocity
and more advanced heuristics.
- Efficient Vision-based Navigation - Learning about the Influence of Motion Blur.
A. Hornung, M. Bennewitz, and H. Strasdat.
In: Autonomous Robots, Vol. 29, Number 2, 2010.
- Learning Adaptive Navigation Strategies for Resource-Constrained Systems.
A. Hornung, M. Bennewitz, C. Stachniss, H. Strasdat, S.Oßwald, and W. Burgard
In: Proceedings of the 3rd International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS), 2010.
- Learning Efficient Policies for Vision-based Navigation.
A. Hornung, H. Strasdat, M. Bennewitz, and W. Burgard.
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.
In our experiments, we used a Pioneer 2 robot with a top-mounted down-looking camera to observe the floor in front of the robot. In the experiment shown in the video below, the robot drives to its goal position using the learned policy. The current camera image with detected and matched landmarks as well as the robot's true (red) and the via UKF estimated (green) pose and the corresponding uncertainty are displayed. Depending on the distance and the angle to the goal as well as on the uncertainty in the belief about the robot's pose, the robot chooses appropriate values for the translational velocity to reach the destination as fast as possible.
We furthermore performed experiments with an outdoor robot and used a scenario in which the robot had to traverse several waypoints. The second part of the video below shows the robot driving to its goal using the learned policy. As can be seen, the robot reaches the goal fast and reliably.