Robot companion: A social-force based approach with human awareness-navigation in crowded environments

Ferrer, Gonzalo; Garrell, Anaís; Sanfeliu, Alberto

doi:10.1109/iros.2013.6696576

Cited by 200 publications

(117 citation statements)

References 13 publications

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“…This situation is called deadlock problem and as discussed in many researches [5,17] that we either develop robot behavior to request the path from human in case of emergency or robot should wait until the possible path is available.…”

Section: One To Many Interaction Resultsmentioning

confidence: 99%

“…Human-robot mutual utilities were considered in the model when both faced with obstacle in the environment. Ferrer et al [17] presented a robot companion approach based on social force concept where interaction force to target human is included. The interactive leaning scheme is also proposed for human to adjust robot motion to meet their own comfortableness.…”

Section: Walking Side By Sidementioning

confidence: 99%

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Social interactive robot navigation based on human intention analysis from face orientation and human path prediction

et al. 2015

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Robot navigation in a human environment is challenging because human moves according to many factors such as social rules and the way other moves. By introducing a robot to a human environment, many situations are expected such as human want to interact with robot or humans expect robot to avoid collision. Robot navigation modeling have to take these factors into consideration. This paper presents a Social Navigation Model (SNM) as a unified navigation and interaction model that allows a robot to navigate in a human environment and response to human according to human intentions, in particular during a situation where the human encounters a robot and human wants to avoid, unavoid (maintain his/her course), or approach (interact) the robot. The proposed model is developed based on human motion and behavior (especially face orientation and overlapping personal space) analysis in preliminary experiments of human-human interaction. Avoiding, unavoiding, and approaching trajectories of humans are classified based on the face orientation and predicted path on a modified social force model. Our experimental evidence demonstrates that the robot is able to adapt its motion by preserving personal distance from passers-by, and interact with persons who want to interact with the robot with a success rate of 90 %. The simulation results show that robot navigated by proposed method can operate in populated environment and significantly reduced the average of overlapping area of personal space by 33.2 % and reduced average time human needs to arrive the goal by 15.7 % compared to original social force model. This work contributes to the future development of a human-robot socialization environment.

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Section: One To Many Interaction Resultsmentioning

confidence: 99%

Section: Walking Side By Sidementioning

confidence: 99%

Social interactive robot navigation based on human intention analysis from face orientation and human path prediction

et al. 2015

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“…The first and the second interactions has been studied in previous papers like [1], [22], [23]. The person-robot interaction parameters {A rp , B rp , λ rp , d rp } were obtained in [24], specifically for our robotic platform.…”

Section: Social-force Modelmentioning

confidence: 99%

Social-aware robot navigation in urban environments

Ferrer¹,

Garrell²,

Sanfeliu³

2013

2013 European Conference on Mobile Robots

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Abstract-In this paper we present a novel robot navigation approach based on the so-called Social Force Model (SFM). First, we construct a graph map with a set of destinations that completely describe the navigation environment. Second, we propose a robot navigation algorithm, called social-aware navigation, which is mainly driven by the social-forces centered at the robot. Third, we use a MCMC Metropolis-Hastings algorithm in order to learn the parameters values of the method. Finally, the validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.

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“…At present, there are methods that produce impressive results in a wide variety of challenging scenarios corrupted by lighting changes, cluttered backgrounds, partial occlusions, viewpoint and scale changes, and large intra-class variations [4,15,25,33,45,48,51] This progress in object recognition has had a positive impact in many application fields such as robotics, where computer vision algorithms have been used for diverse robotics tasks such as object recognition and grasping [3,5], detection and tracking of people in urban settings [10,34,39], human-robot interaction [40,47], and robot localization and navigation [11,16,26].…”

Section: Introductionmentioning

confidence: 99%

Interactive multiple object learning with scanty human supervision

Villamizar

Garrell

Sanfeliu

et al. 2016

Computer Vision and Image Understanding

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We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human-robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds.

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Robot companion: A social-force based approach with human awareness-navigation in crowded environments

Cited by 200 publications

References 13 publications

Social interactive robot navigation based on human intention analysis from face orientation and human path prediction

Social interactive robot navigation based on human intention analysis from face orientation and human path prediction

Social-aware robot navigation in urban environments

Interactive multiple object learning with scanty human supervision

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