A new approach to improve robot navigation in crowded environments

While robots have become increasingly advanced over the past few years, most of them are still unable to reliably navigate very crowded spaces, such as public areas or roads in urban environments. To be implemented on a large-scale and in the smart cities of the future, however, robots will need to be able to navigate these environments both reliably and safely, without colliding with humans or nearby objects. Researchers at the University of Zaragoza and the Aragon Institute of Engineering Research in Spain have recently proposed a new machine learning–based approach that could improve robot navigation in both indoor and outdoor crowded environments. This approach, introduced in a paper pre-published on the arXiv server, entails the use of intrinsic rewards, which are essentially "rewards" that an AI agent receives when performing behaviors that are not strictly related to the task it is trying to complete. "Autonomous robot navigation is an open unsolved problem, especially in unstructured and dynamic environments, where a robot has to avoid collisions with dynamic obstacles and reach the goal," Diego Martinez Baselga, one of the researchers who carried out the study, told Tech Xplore.