Human-Aware Navigation Planner for Diverse Human-Robot Contexts

TL;DR

This paper introduces a flexible human-aware navigation planner for robots that adapts to various indoor and outdoor human-robot interaction contexts, validated through simulations and real-world deployment.

Contribution

It presents a tunable navigation planner capable of handling diverse human-robot contexts, with detailed architecture, implementation, and validation in real-world scenarios.

Findings

Effective handling of multiple human-robot contexts demonstrated in simulations.

Quantitative analysis shows improved navigation performance.

Successful deployment on a real robot confirms practical applicability.

Abstract

As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of humanrobot contexts. We present the architecture of the planner and discuss the features and some implementation details. Then we present a detailed analysis of various simulated humanrobot contexts using the proposed planner along with some quantitative results. Finally, we show the results in a real-world scenario after deploying our system on a real robot.