The Wayfarer modular navigation payload for intelligent robot infrastructure

We are currently developing autonomous urban navigation capabilities for the iRobot PackBot. The TARDEC-funded Wayfarer Project is developing a modular navigation payload that incorporates LIDAR, vision, FLIR, and inertial navigation sensors. This payload can be attached to any PackBot and will provide the robot with the capability to perform fully-autonomous urban reconnaissance missions. These capabilities will enable the PackBot Wayfarer to scout unknown territory and return maps along with video and FLIR image sequences. The Wayfarer navigation payload includes software components for obstacle avoidance, perimeter and street following, and map-building. The obstacle avoidance system enables the PackBot to avoid collisions with a wide range of obstacles in both outdoor and indoor environments. This system combines 360-degree planar LIDAR range data with 3D obstacle detection using stereo vision using a Scaled Vector Field Histogram algorithm. We use a real-time Hough transform to detect linear features in the range data that correspond to building walls and street orientations. We use the LIDAR range data to build an occupancy grid map of the robot's surroundings in real-time. Data from the range sensors, obstacle avoidance, and the Hough transform are transmitted via UDP over wireless Ethernet to an OpenGL-based OCU that displays this information graphically and in real-time.

[1]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[2]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  Koren,et al.  Real-Time Obstacle Avoidance for Fast Mobile Robots , 2022 .

[4]  Brian Yamauchi,et al.  PackBot: a versatile platform for military robotics , 2004, SPIE Defense + Commercial Sensing.

[5]  Yoram Koren,et al.  Real-time obstacle avoidance for fact mobile robots , 1989, IEEE Trans. Syst. Man Cybern..

[6]  Dana H. Ballard,et al.  Computer Vision , 1982 .