View Proposal

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Proposer

Daniel Hernandez Garcia

Title

Social Navigation Strategies for Social Robots

Goal

Develop a social-human-aware navigation controller for a robot in order to safely navigate around a crowded environment.

Description

Robots are becoming more prevalent in our society and being able to move through a crowded room or corridor is one of the most fundamental and basic tasks that these robots should be able to accomplish. Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding [1]. Looking at current applications of robots, it is easy to see that a lot of them are still working in fenced off areas or even inside cages. One of the most commonly used examples is logistics where robots either pick and place items on shelves or, because picking up things is hard, drive the whole shelf to a person to do the picking [2]. All of this happens in very structured and unpopulated environments. Existing navigation systems still face real-world challenges when deployed in the wild [3]. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive [4]. If we ever want robots to be able to move outside of these fenced-off areas, we need to make sure that they move in a safe manner. This is normally achieved by off the shelve navigation and localisation methods such as the ROS [5] navigation stack [6]. Mind you, of similar importance is that humans feel safe around the moving robot and the movement being safe and being perceived as safe is not always the same. In this project, we are looking for someone who wants to enable a robot (for example, the ARI robot [7]) to navigate a room, with humans, reliably, safely, and with perceived safety. This will require the set-up of the ROS navigation stack on the system and the implementation of a human-aware planner. There are off the shelve methods that can be used [8][9] but more advanced solutions can be implemented as well [3][4]. The resulting system would then be evaluated with participants either face to face or using videos [10]. [1] Core challenges of social robot navigation: A survey. https://arxiv.org/abs/2103.05668 [2] https://www.youtube.com/watch?v=HSA5Bq-1fU4 [3] Augmented Social Force Model for Legged Robot Social Navigation https://rpl-cs-ucl.github.io/ASFM/ [4] Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation https://arxiv.org/abs/2209.10780 [5] https://www.ros.org/ [6] http://wiki.ros.org/navigation [7] https://pal-robotics.com/robots/ari/ [8] http://wiki.ros.org/social_navigation_layers [9] https://docs.nav2.org/ [10] A Protocol for Validating Social Navigation Policies https://arxiv.org/abs/2204.05443

Resources

Background

Url

Difficulty Level

Challenging

Ethical Approval

Full

Number Of Students

2

Supervisor

Daniel Hernandez Garcia

Keywords

Degrees