This video demonstrates the reliable navigation of a smart wheelchair system (SWS) in an urban environment. Urban environments present unique challenges for service robots. They require localization accuracy at the sidewalk level, but compromise estimated GPS positions through significant multipath effects. However, they are also rich in landmarks that can be leveraged by feature-based localization approaches. To this end, the SWS employed a map-based approach. A map of South Bethlehem was acquired using a server vehicle, synthesized a priori, and made accessible to the SWS client. The map embedded not only the locations of landmarks, but also semantic data delineating seven different landmark classes to facilitate robust data association. Landmark segmentation and tracking by the SWS was then accomplished using both 2-D and 3-D LIDAR systems. The resulting localization algorithm has demonstrated decimeter-level positioning accuracy in a global coordinate frame. The localization package was integrated into a ROS framework with a sample-based planner and control loop running at 5 Hz. For validation, the SWS repeatedly navigated autonomously between Lehigh University's Packard Laboratory and the University bookstore, a distance of approximately 1.0 km roundtrip.

This experiment demonstrates how a reactive navigation method successfully enables our robotic wheelchair SENA to navigate reliably in the entrance of our building at the University of Malaga (Spain). The robot navigates autonomously amidst dozens of students while avoiding collisions. The method is based on a space transformation, which simplifies finding collision-free movements in real-time despite the arbitrarily complex shape of the robot and its kinematic restrictions.

For people with extreme disabilities such as ALS or quadriplegia, it is often hard to move about on their own and interact with their environments due to their immobility. Our work - nicknamed "Project Chiron" - attempts to alleviate some of this immobility with a kit that can be used on any Permobil-brand wheelchair.

The maturity of the tools developed for mobile-robot navigation and explained in this chapter have enabled Google to integrate them into an experimental vehicle. This video demonstrates Google's self-driving technology on the road.

We present a mobile-robot navigation system guided by a novel vision-based, road-recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long-term drift from its original lateral position. We integrate odometry and our visual, road-recognition system into a grid-based local map which estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25 076 images to within 11.42 cm (with respect to roads that are at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving on a busy college campus without failure. This demonstrates the robustness of the proposed approach to handle challenges including occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle objects.

This video demonstrates a fully autonomous navigation solution for mobile robots operating in urban pedestrian areas. Path planning is performed by a graph search on a discretized grid of the workspace. Obstacle avoidance is performed by a slightly modified version of the dynamic-window approach.

This video shows the vision-based autonomous navigation of a Cycab mobile vehicle able to avoid obstacles detected by its laser range finder. The reference trajectory is provided as a sequence of previously-acquired key images. Obstacle avoidance is based on a predefined set of circular avoidance trajectories. The best trajectory is selected when an obstacle is detected by the laser scanner.