Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Vega, Araceli; Cintas, Ramón; Manso, Luis J.; Bustos, Pablo; Núñez, Pedro

doi:10.1007/978-3-030-36150-1_53

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…However, since it requires knowing the perfect path to the goal, we argue that it applies only to very small or perfectly known environments. Therefore, in our benchmark, we use the cumulative heading change metric, m chc (1), as in [24], [30], and [31].…”

Section: ) Cumulative Heading Changementioning

confidence: 99%

Quantitative Metrics for Benchmarking Human-Aware Robot Navigation

Karwowski

Szynkiewicz

2023

IEEE Access

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Social robots have recently gained popularity, and many human-aware navigation approaches have emerged. This work presents a comprehensive benchmark for quantitatively assessing robot navigation methods. As an automated quantitative approach, our Social Robot Planner Benchmark (SRPB) produces invariable indicators of the algorithm's performance that can assist the system designer in selecting the best method for the specific application. Our benchmark extends state-of-the-art task performance scores and proposes novel social metrics regarding robot motion naturalness and the perceived safety of humans surrounding the robot. Our social metrics take human tracking reliability into account. Using the SRPB integrated with the TIAGo robot, we assessed the robot's behaviour operating with traditional and human-aware trajectory planners in simulated and real-world environments. Our experiments tested whether state-ofthe-art human-aware trajectory planners significantly improve human-awareness indicators over traditional approaches yet still maintain reasonable navigation performance. An open-source implementation of our benchmark, compatible with the Robot Operating System, is provided.

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Section: ) Cumulative Heading Changementioning

confidence: 99%

Quantitative Metrics for Benchmarking Human-Aware Robot Navigation

Karwowski

Szynkiewicz

2023

IEEE Access

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“…Social interaction spaces are widely modeled by using Gaussian functions [21,30,47,48]. So, since spaces occupied by humans and robots can be modeled as two-dimensional ones, it is common to model those spaces with two-dimensional Gaussian functions, which are expressed as follows:…”

Section: Asymmetric Gaussian Functionmentioning

confidence: 99%

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions

Sousa

Barrios-Aranibar

Díaz-Amado

et al. 2022

Sensors

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Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.

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“…The ideal is that the robot does not invade the intimate human space and keeps a constant and soft velocity, but not too slow to create a discomfort or get its task late. Social interaction spaces are widely modelled by using Gaussian functions [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

et al. 2021

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human–Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.

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Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Cited by 8 publications

References 14 publications

Quantitative Metrics for Benchmarking Human-Aware Robot Navigation

Quantitative Metrics for Benchmarking Human-Aware Robot Navigation

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

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