Defining Adaptive Proxemic Zones for Activity-Aware Navigation

Clavero, Jonatan Ginés; Rico, Francisco Martín; Rodríguez-Lera, Francisco J.; Hernández, José Miguel Guerrero; Olivera, Vicente Matellán

doi:10.1007/978-3-030-62579-5_1

Cited by 8 publications

(15 citation statements)

References 20 publications

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“…We take as a baseline the results from our previous work [10], PDDL + DWB. Using PDDL in one loop and Behavior Trees in the other, in combination with DWB and TEB, PDDL + TEB shows a navigation time lower than the time used by the Behavior Trees option, Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Firstly, the results obtained show a relationship between the navigation algorithm that hides the navigation component Nav2 and robot performance. Our previous experiment in [10] presented a performance of 80 s using DWB version. This study obtained an average of 35 and 41 s using TEB.…”

Section: Discussionmentioning

confidence: 99%

“…The Nav2 system was designed for a high degree of customizability and future expansion. Because of this, we have chosen to integrate the proposed human representation framework as a cost map layer, showing in-depth in [10].…”

Section: Navigationmentioning

confidence: 99%

“…This work uses the framework proposed in [10] for representing the space surrounding a person, the proxemic areas, on a cost map. This representation is fundamental to differentiate humans from the rest of the obstacles, thus enriching the robot's knowledge of its environment.…”

Section: Human-aware Navigation Componentmentioning

confidence: 99%

“…Besides, the study presented in [10] proposed a framework for adapting individuals' proxemic shape and added a new layer called the cooperation zone, answering Research Question 1. That work measured the time spent in different proxemic zones during the human-robot interaction.…”

Section: Introductionmentioning

confidence: 99%

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Impact of decision-making system in social navigation

Clavero

Rico

Rodríguez-Lera

et al. 2022

Multimed Tools Appl

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Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot’s behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones’ information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Navigationmentioning

confidence: 99%

Section: Human-aware Navigation Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of decision-making system in social navigation

Clavero

Rico

Rodríguez-Lera

et al. 2022

Multimed Tools Appl

Self Cite

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Benchmarking Off-the-Shelf Human-Aware Robot Navigation Solutions

Gouguet,

Karami,

Lozenguez

et al. 2024

Lecture Notes in Networks and Systems

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In recent years, the number of robotic applications in public spaces has been growing. Decades of research have given rise to various methods of human-aware robotic navigation. There are a lot of different navigation solutions to guide a robot in presence of humans. Despite multiple surveys comparing existing navigation solutions, few of them take social criteria into account. In this sense, it is difficult to evaluate existing methods and select the one that performs better in a given context. In this article, we first provide a thorough classification of state-of-the-art solutions regarding human-aware robotic navigation solutions. Then, we select a set of measurable criteria to evaluate both the efficiency and the social-compliance of navigation solutions. Using these criteria, we finally compare representative off-the-shelf navigation solutions using the SEAN Simulator to identify the most suitable for human-aware navigation.

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A kinematic model generates non-circular human proxemics zones

Camara,

Fox

2023

Advanced Robotics

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Defining Adaptive Proxemic Zones for Activity-Aware Navigation

Cited by 8 publications

References 20 publications

Impact of decision-making system in social navigation

Impact of decision-making system in social navigation

Benchmarking Off-the-Shelf Human-Aware Robot Navigation Solutions

A kinematic model generates non-circular human proxemics zones

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