Online Social Robot Navigation in Indoor, Large and Crowded Environments

Abstract

New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against stateof-the-art approaches in challenging simulated scenarios, and we also demonstrate its feasibility with the Pepper robot in real-world trials.

Ring attractor bio-inspired neural network for social robot navigation

“…The navigation system is divided into perception, planning, and control stages (as proposed in our previous work Silva et al, 2022Silva et al, , 2023. In the perception stage, data from the environment is obtained and processed.…”

Corrigendum: Ring attractor bio-inspired neural network for social robot navigation

“…The target position is set in the target_setter node. In each integration step, the ROS_BINNF node sends information about the obstacles, target, and current state of the robot, along with the last step neural "The navigation system is divided into perception, planning, and control stages (as proposed in our previous work Silva et al, 2022Silva et al, , 2023. In the perception stage, data from the environment is obtained and processed.…”