Socially-Aware Navigation: A Non-linear Multi-Objective Optimization Approach

TL;DR

This paper extends a social navigation planner based on multi-objective optimization to handle multiple scenarios and objectives, demonstrating its effectiveness on simulated and real robots in diverse human environments.

Contribution

It introduces a multi-objective PaCcET-based SAN planner integrated into ROS, capable of managing complex social navigation scenarios with multiple objectives.

Findings

Successfully navigated diverse social scenarios including galleries and queues.

Validated approach on both simulated and real robots.

Handles multiple objectives and robot types effectively.

Abstract

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so that the robot does not invade the personal space of people. This paper extends our PaCcET based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System's (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios like hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).