Socially Aware Motion Planning for Service Robots Using LiDAR and RGB-D Camera

TL;DR

This paper presents a social-aware motion planning system for service robots that integrates human position, velocity, and personal space modeling using LiDAR and RGB-D camera data, enabling safe and comfortable navigation.

Contribution

It introduces a novel social-aware navigation framework combining human state estimation, personal space modeling, and dynamic trajectory generation.

Findings

Robot successfully navigates alongside humans respecting personal space.

Fusion of LiDAR and RGB-D improves human detection accuracy.

The system maintains safety and comfort in dynamic environments.

Abstract

Service robots that work alongside humans in a shared environment need a navigation system that takes into account not only physical safety but also social norms for mutual cooperation. In this paper, we introduce a motion planning system that includes human states such as positions and velocities and their personal space for social-aware navigation. The system first extracts human positions from the LiDAR and the RGB-D camera. It then uses the Kalman filter to fuse that information for human state estimation. An asymmetric Gaussian function is then employed to model human personal space based on their states. This model is used as the input to the dynamic window approach algorithm to generate trajectories for the robot. Experiments show that the robot is able to navigate alongside humans in a dynamic environment while respecting their physical and psychological comfort.