Difference: FRCVPublicProject (11 vs. 12)

Wide Area Visual Navigation (WAVN)

We are investigating a novel approach to navigation for a heterogenous robot team operating for long durations in an environment where there is long and short term visual changes. Small-scale precision agriculture is one of our main applications: High precision, high reliability GPS can be an entrance barrier for small family farms. A successful solution to this challenge would open the way to revolutionizing small farming to compete with big agribusiness. The challenge is enormous however. A family farm operating in a remote location, experiencing all the changes in terrain appearance and navigability that comes with seasonal weather changes and dramatic weather events. Our work is a step in this direction.

Our approach is based on visual homing, which is a simple and lightweight method for a robot to navigate to a visually definied target that is in its current field of view. We extend this approach to work with targets that are beyond the current field of view by leveraging visual information from all camera assets in the entire team (and potentially fixed camera assets on buildings or other available camera assets). To ensure efficient and secure distributed communication between the team, we employ distributed blockchain communication: Team members regularly upload their visual panorama to the blockchain, which is then available to all team members in a safe and secure fashion. When a robot needs to navigate to a distant target, it queries the visual information from the rest of the team, and establishes a set of intermediate visual targets it can sequentially navigate to using homing, ending with the final target.

For a short video introduction, see here.

We are also investigating the synergy of blockchain and navigation methodologies to show that blockhain can be used to simplify navigation in addition to providing a distributed and secure communication channel.

Key to the WAVN navigation approach is establishing an intermediate sequence of landmarks by looking for a chain of common landmarks between pairs of robots. We have investigated different ways in which robots can identify whether they are seeing the same landmark and we show that using a CNN-bsed YOLO to segement a scene into common objects followed by feature matching to identify whether objects are the same outperforms featrure matching on its own. Furthermore using a group of objects as a landmark outperforms a single object landmark. We are currently investigating whether object diversity in a group improves this even further.

We have conducted WAVN experiments using grid-simulations, ROS/Gazebo simulations and lab experiments.using Turtlebot3 and Pioneer robots. The image above shows a Gazebo simulation of 3 pioneer robots in a small outdoor work area with plenty of out of view targets to navigate to. Our next step is to use WAVN for outdoor navigation to campus locations using a team of Pioneer robots.

Multilingual Software Analysis (MLSA)