Alternating between independent and cooperative behavior allows robots to find their way through crowds

Robots can navigate efficiently through crowds of people by cleverly alternating between independent and cooperative behavior, and in such a way that they disturb the people around them as little as possible. This is the result of a study by TU Professor Roderich Groß posted to the arXiv preprint server.

“Whether in factories, hospitals or disaster areas—robot groups will increasingly operate in human environments,” explains Groß, who is head of the Resilient Cyber-Physical Systems department at the TU’s Department of Computer Science. “One problem that has not yet been fully solved is how they can do this safely and efficiently, especially in fairly dense crowds.”

Together with researchers from the University of Sheffield, his team has just presented a study on this topic at the Distributed Autonomous Robotic Systems conference (DARS 2024) in New York.

The researchers simulated groups of robots encountering different types of crowds. In the simulations, the people either stood still, moved toward the robots or crossed their path at right angles. In addition, the tests were carried out with different numbers of people moving in the same space as the robots.

Three behavioral strategies were tested: The independent strategy, in which each robot independently makes its way through the crowd; the cooperative strategy, in which the robots form a line and follow the robot in the front; and the adaptive strategy, in which the robots switch autonomously between the first two strategies depending on the situation.

The simulations with up to 200 robots showed that the they that used the cooperative strategy to move through crowds of people standing or approaching them caused fewer disturbances and that this strategy proved to be more effective in dense crowds. The independent strategy, on the other hand, was superior when there were only a few people in the vicinity of the robots.

In situations where people were moving at right angles to the robots, the independent strategy was more successful in both aspects. The adaptive strategy combined the advantages of both approaches and performed particularly well in all scenarios without greatly affecting the crowds.

“The cooperative strategy proved effective for robots moving through crowds, with the added benefit of maintaining the communication network among the robots,” explains Jahir Argote Gerald, first author of the study.

“Still, we found conditions where robots that navigate the crowds on their own outperform robots that navigate the crowds cooperatively, leading us to create an adaptive strategy that performed well and did not cause too much disruption to the crowds in all conditions we considered.”

Next, the scientists plan to develop formal safety guarantees through improved methodologies and validate the results in real-world environments. As the results so far are based on simulated crowds, further research is needed to transfer the methods to navigation through real crowds.

In this case, the robots will have to determine the positions of the people independently and in real time. It must also be ensured that the robots behave safely even if the behavior of individual people differs greatly from that of others in the crowd.