Human Partner and Robot Guide Coordination System Under Social Force Model Framework Using Kinect Sensor

Mu'allimi, Hari Malik; Dewantara, Bima Sena Bayu; Pramadihanto, Dadet; Marta, Bayu Sandi

doi:10.1109/ies50839.2020.9231872

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Where T v is the unit direction vector. In summary, the resultant force on the robot is shown in formula (7).…”

Section: ( ) ( ) 0 ( )mentioning

confidence: 99%

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Research on manned robot path expansion based on mixed social force model

Gao,

Huang,

Feng

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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Aiming at the problem of how to safely and efficiently pass the manned robot in the dense crowd, the path extension algorithm based on the mixed social force model are studied. Firstly, starting from the traditional social force model, a mixed social force model is established in the scene of the integration of pedestrians and robots. Secondly, to address the problem of blocking the passage of robots in dense crowd, the interaction behavior is designed to trigger the movement of individual pedestrians, so that the distribution situation of the crowd that hinders the passage of robot is changed. Finally, guide the robot through the crowd according to the expansion of path until it reaches the destination. Simulation experiments show that the proposed algorithm can improve the passing efficiency of robot in indoor dynamic and static dense crowd environment.

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“…Where T v is the unit direction vector. In summary, the resultant force on the robot is shown in formula (7).…”

Section: ( ) ( ) 0 ( )mentioning

confidence: 99%

“…When the obstacle is behind the agent's movement, it will no longer affect its movement, so the repulsion force is no longer calculated at this time. Improvements to formulas (7) and ( 8) are as follows.…”

Section: ( ) ( ) 0 ( )mentioning

confidence: 99%

Research on manned robot path expansion based on mixed social force model

Gao,

Huang,

Feng

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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show abstract

“…However, autonomous vehicles will be sharing urban roads with human tra c participants for a long time in the future. Autonomous vehicles running on urban roads need to be able to autonomously interact with pedestrians and must be able to seize road resources [6][7][8]. Especially at urban intersections with complex tra c, the problem of humanvehicle interaction becomes more serious, making it di cult for autonomous vehicles to land and drive [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on the Behavioral Dynamics Motion Planning Method of the Human-Vehicle Social Force Model

Gai-ning

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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The interactive motion planning between unmanned vehicles and pedestrians in urban road environments is the key to realizing the autonomous motion of unmanned vehicles in hybrid traffic scenarios. The problem of human-vehicle interaction motion planning modeling at complex intersections is studied for an unmanned vehicle in this article. First, the motion planning of pedestrians and the unmanned vehicles is established according to the social force model and the behavioral dynamics model. Then, the autonomous vehicle is added to the crowd, and the human-vehicle interaction force is established. The virtual force is added to the social force model and the behavioral dynamics model, respectively, and the improved social force model and the behavioral dynamics model are used for the motion planning of pedestrians and unmanned vehicles. In this way, the established model solves the problems of simple pedestrian interaction motion planning in the social force model and single-body motion planning in the behavioral dynamics and thus provides a strong support for multibody motion planning. Finally, through the interactive motion planning trajectory of pedestrians and unmanned vehicles in different scenes, the vehicle and pedestrian motion planning trajectory can effectively avoid overlapping or crossing, so as to avoid the collision, which verifies the effectiveness and feasibility of the proposed model.

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“…Several studies are implementing Social Force Model (SFM) into a mobile robot to navigate (Tamura, et al, 2010) (Yang, et al, 2019). Besides that, several applications of SFM are used for other purposes, such as companion robots (Ferrer, et al, 2013), the human leader following robot (Kuderer & Burgard, 2014), human guide robot (Dewantara & Miura, 2017) (Muallimi, et al, 2020), human-robot collision avoidance (Ratsamee, et al, 2013), social robot navigation (Kivrak, et al, 2021). Other researchers have developed SFM applications in tour-guide robots (Bellarbi, et al, 2017), healthcare robot navigation (Rifqi, et al, 2021), and SFM in quadcopter robots (Gil, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model

2022

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Membangun sebuah sistem navigasi pada mobile robot yang bergerak di ruang sosial perlu memperhatikan beberapa aspek krusial, seperti menghindari rintangan, menjaga arah hadap robot ke tujuan, dan mencapai tujuan dengan cepat. Penelitian ini bertujuan untuk mengembangkan sistem navigasi pada Omnidirectional mobile robot menggunakan Fuzzy-Social Force Model (FSFM). Social Force Model (SFM) mampu menggerakan robot ke tujuan sambil menghindari rintangan. Fuzzy Inference System (FIS) digunakan untuk menghasilkan gain adaptif sebagai salah satu parameter SFM agar respon SFM sesuai dengan masukan dari sensor lidar. Aturan FIS dioptimasi agar mendapatkan nilai optimal menggunakan Particle Swarm Optimization (PSO). Dari hasil percobaan, mobile robot mencapai tujuan lebih cepat dengan selisih 1.59 s dan nilai error heading robot lebih kecil 0.9261 dibandingkan FSFM tanpa optimasi.

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Human Partner and Robot Guide Coordination System Under Social Force Model Framework Using Kinect Sensor

Cited by 5 publications

References 5 publications

Research on manned robot path expansion based on mixed social force model

Research on manned robot path expansion based on mixed social force model

Research on the Behavioral Dynamics Motion Planning Method of the Human-Vehicle Social Force Model

Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model

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