Interactive Navigation of Mobile Robots Based on Human’s Emotion

Jiang, Rui; Ge, Shuzhi Sam; Tangirala, Nagacharan Teja; Lee, Tong Heng

doi:10.1007/978-3-319-47437-3_24

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…One popular potential field approach is the Artificial Potential Field (APF) method (Jiang et al 2016). The APF method has been widely used in robot navigation and extended to dynamic environments by incorporating timevarying potentials and obstacle-avoidance strategies (Ferrara and Rubagotti 2007).…”

Section: Local Motion Generationmentioning

confidence: 99%

“…sitting, standing, moving) as well as their potential interactions with objects to represent their personal space. Although not using a Gaussian modeling approach, other proposals build personal space around humans considering other factors as well, such as the person's emotion (Bera et al 2019;Jiang et al 2016) or the specific area where the person is located (Lu and Smart 2013). In addition to being a frequently used tool for modeling people's personal space, Gaussian modeling has also been applied to estimate the interaction space of groups of people (Lobato et The inclusion of proxemics in many proposals on socially aware navigation focuses on defining forbidden or inappropriate areas for navigation that the robot must avoid crossing.…”

Section: Social Normsmentioning

confidence: 99%

“…The perception modules of robots can provide information about certain human characteristics (age, gender, height, etc.) that can be used to filter or shift the robot's behavior according to the type of human it is interacting with (Bera et al 2019;Jiang et al 2016;Patompak et al 2016). With the ever-expanding applications of Artificial Intelligence in the computer vision community, we believe that the online realization of adaptive robot behaviors is not very far.…”

Section: Proposalsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on socially aware robot navigation: Taxonomy and future challenges

Singamaneni,

Bachiller-Burgos,

Manso

et al. 2024

The International Journal of Robotics Research

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Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field.

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Section: Local Motion Generationmentioning

confidence: 99%

Section: Social Normsmentioning

confidence: 99%

Section: Proposalsmentioning

confidence: 99%

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A survey on socially aware robot navigation: Taxonomy and future challenges

Singamaneni,

Bachiller-Burgos,

Manso

et al. 2024

The International Journal of Robotics Research

View full text Add to dashboard Cite

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“…erefore, this paper focuses on the indoor navigation of these robots, especially the efficiency of robotic navigation. e methods for autonomous navigation can be classified as the model-based approaches [4][5][6] and the model-free approaches [7].…”

Section: Introductionmentioning

confidence: 99%

Fast Transfer Navigation for Autonomous Robots

Wang

Tao

et al. 2021

Journal of Robotics

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Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

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