Towards efficient human-robot cooperation for socially-aware robot navigation in human-populated environments: the SNAPE framework

Vega-Magro, A.; Gondkar, Raju Ramakrishna; Manso, Luis J.; Núñez, Pedro

doi:10.1109/icra48506.2021.9561448

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…However, the complexity and variety of human behavior in the real world can make this assumption fail. Recent approaches propose that the mobile robot can interact with people not only because their paths may cross on the map, but more actively, through gestures, vocalization and touch, to require their help in navigating [16,120,121]. As a signaling mechanism for conveying an intention to humans, incorporating features such as body posture and gestures also contributes to making the robot appear more friendly and predictable to humans, leading to better human-robot interactions and an overall improved experience.…”

Section: Discussionmentioning

confidence: 99%

“…inducible SFM (i-SFM) for predicting human motion. Dugas et al [120] The IAN framework Interaction actions (saying, touching, and gesturing) for navigating in crowded scenarios Vega-Magro et al [121] The SNAPE framework CORTEX cognitive software architecture…”

Section: Reference Methodsmentioning

confidence: 99%

“…The first of the integration options discussed above is the one used by Kamezaki et al [16] and Dugas et al [120]. The second is the one proposed by Vega-Magro et al [121].…”

Section: Multi-behavior Navigation Approachesmentioning

confidence: 99%

“…The SNAPE framework [121] emphasizes that a robot needs social behaviors and cooperation from humans to navigate socially. The core of the proposal is the use of the CORTEX software architecture for robotics [125,126].…”

Section: Multi-behavior Navigation Approachesmentioning

confidence: 99%

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Evolution of Socially-Aware Robot Navigation

et al. 2023

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In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans.

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Section: Discussionmentioning

confidence: 99%

Section: Reference Methodsmentioning

confidence: 99%

“…The first of the integration options discussed above is the one used by Kamezaki et al [16] and Dugas et al [120]. The second is the one proposed by Vega-Magro et al [121].…”

Section: Multi-behavior Navigation Approachesmentioning

confidence: 99%

Section: Multi-behavior Navigation Approachesmentioning

confidence: 99%

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Evolution of Socially-Aware Robot Navigation

et al. 2023

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“…This is done in works like the one presented by Zhou et al [19], where the On-line Collision Avoidance for Dynamic Vehicles is presented, which uses Buffered Voronoi Cells to derive an optimal avoidance model. Moreover, Vega-Magro [20] proposes a solution that integrates multiple layers of information. In particular, a social layer is defined, where the detection of people is performed.…”

Section: Related Workmentioning

confidence: 99%

Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Medina-Sánchez,

Janzon,

Zella

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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