Four U.S. Army Research Laboratory researchers have developed an algorithm that will make it easier for the Department of Defense to maintain wirelessly networked Army PackBots and other military assets using radio communications.
The team recently demonstrated they could map the region of good connectivity to a radio base station using received signal strength.
“We are working on fundamental techniques that employ autonomous agents to maintain connectivity, and continuously provide situational awareness to Soldiers,” said Dr. Brian Sadler of ARL’s Computational and Information Sciences Directorate in a recent article about the research.
The team has been focused on radio connectivity between robots for nearly two years, he said.
“We can find and explore areas that have high RSS and then map these areas as having the strongest connectivity to the radio base station,” said Jeffrey Twigg, a contract employee with ARL’s Computational and Information Sciences Directorate who was instrumental in this research. “This brings us a step closer to operating autonomous systems in complex and unstructured situations like those soldiers encounter on the battlefield.”
When the environment is open, communication between autonomous robots is well understood. Indoors however, walls and other sources of interference cause radio propagation to be more complex. This requires the communication strategies used by robotic systems to be more complex, Twigg said.
“Ultimately we want to form building blocks that increase the effectiveness of a networked team of robots in an unknown environment,” Twigg said. “If robots can be programmed to map where there is the potential to communicate inside a building, then Soldiers and other assets can know where in the building they will be able to communicate with a radio base station.”
Efficient Base Station Connectivity Region Discovery by Jeffrey Twigg, Dr. Jonathan Fink, Dr. Paul Yu and Dr. Brian Sadler is a project that takes a second step toward a broad understanding of solutions for Army robotics. The study has been submitted for publication by the International Journal of Robotics Research.
The researchers took their findings from earlier research conducted this year to the next level. They combined region decomposition and RSS sampling to form an efficient graph search. The nominal RSS in a sampling region is obtained by averaging local RSS samples to reduce the small scale fading variation.
At this point, the system has been tested in the lab as well as at the MOUT site at Fort Indiantown Gap.
The algorithm can be used for sensing and collaborative autonomy within the region of base station connectivity, Twigg said.
The ARL researchers first presented the development: RSS Gradient-Assisted Frontier Exploration and Radio Source Localization at the 2012 International Conference on Robotics and Automation in St. Paul, Minn.