To operate efficiently in urban environments, mobile robots and other autonomous systems should be able to move safely on sidewalks and avoid collisions with pedestrians or other obstacles. This is particularly true for delivery robots or systems that are specifically programmed to patrol urban environments. Researchers at Georgia Institute of Technology and Stanford University have recently developed AlienGo, a quadruped robot that can follow specific routes generated by public map services while remaining on sidewalks and avoiding collisions with obstacles or humans. This robot, presented in a paper pre-published on arXiv, is based on a new, highly performing two-staged learning framework for safe sidewalk navigation. “As part of this project, we developed an intelligent quadrupedal robot that can navigate sidewalks in the real world,” Sehoon Ha, one of the researchers who carried out the study, told TechXplore. “Our work is inspired by two stems of the existing work: Autonomous driving and indoor robot navigation. However, as outdoor sidewalk navigation typically takes place in unstructured environments with a wide variety of pedestrians and obstacles without any guide lanes, we also proposed a set of learning techniques and algorithms to solve these specific challenges.”
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