Enhancing Social Robot Navigation with Integrated Motion Prediction and Trajectory Planning in Dynamic Human Environments

TL;DR

This paper presents an integrated approach combining motion prediction and trajectory planning to improve social robot navigation in dynamic human environments, ensuring safety and social acceptability.

Contribution

It introduces a novel method that incorporates human interactive information into trajectory planning, enhancing safety and social compliance in robot navigation.

Findings

Superior obstacle avoidance performance

Effective integration of human motion data

Open-source implementation available

Abstract

Navigating safely in dynamic human environments is crucial for mobile service robots, and social navigation is a key aspect of this process. In this paper, we proposed an integrative approach that combines motion prediction and trajectory planning to enable safe and socially-aware robot navigation. The main idea of the proposed method is to leverage the advantages of Socially Acceptable trajectory prediction and Timed Elastic Band (TEB) by incorporating human interactive information including position, orientation, and motion into the objective function of the TEB algorithms. In addition, we designed social constraints to ensure the safety of robot navigation. The proposed system is evaluated through physical simulation using both quantitative and qualitative metrics, demonstrating its superior performance in avoiding human and dynamic obstacles, thereby ensuring safe navigation. The implementations are open source at: \url{https://github.com/thanhnguyencanh/SGan-TEB.git}