Movement Control of Accompanying Robot Based on Artificial Potential Field Adapted to Dynamic Environments

Nakazawa, Kazushi; Takahashi, Keita; Kaneko, Masahide

doi:10.1541/ieejeiss.134.293

Cited by 4 publications

(8 citation statements)

References 7 publications

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“…Human-Robot Collaborative Navigation One of the first faced humanrobot collaborative challenges was side-by-side navigation [18,19]. In parallel, [8,9,11] approached this challenge through Social Force Model (SFM) methods and, in another context, [23,16] presented methods for side-by-side wheelchair navigation.…”

Section: Human-robot Collaborationmentioning

confidence: 99%

Human-Robot Collaborative Navigation Search Using Social Reward Sources

Dalmasso

Garrell

Jiménez

et al. 2019

Advances in Intelligent Systems and Computing

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This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: humanrobot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.

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Section: Human-robot Collaborationmentioning

confidence: 99%

Human-Robot Collaborative Navigation Search Using Social Reward Sources

Dalmasso

Garrell

Jiménez

et al. 2019

Advances in Intelligent Systems and Computing

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“…To guide the users in a social manner, the robot should adjust its motion to adapt to the needs of the users and support their activities. The adaptive motion control of the robot is based on the artificial potential field method . The attractive and repulsive forces from the environments are generated as virtual artificial potential fields, and the motion of the robot is controlled by the gradient of the potentials.…”

Section: Framework For Adaptive Motion Control Of Sociable Guide Robotmentioning

confidence: 99%

“…In order to prevent the robot from colliding with obstacles and pedestrians, we set a repulsive potential for them in the same way as in previous work . The repulsive force from each point of the objects is set as

F_{r} = \frac{k_{r}}{{(d_{r} - d_{0})}^{2}}, if d_{r} > d_{0}; F_{r} = \infty, else

where k r is the repulsion coefficient for the objects, d r is the distance from the object to the robot, and d 0 is the minimum distance to be traveled by the robot to reach the object.…”

Section: Framework For Adaptive Motion Control Of Sociable Guide Robotmentioning

confidence: 99%

“…With our framework, the adaptive motions are formed automatically, and all four above‐mentioned functions for a sociable guide robot can be realized. We propose generating special artificial potential fields for the users and goal separately, and we integrate them with the basic potential fields generated from the other obstacles. The work of Nakazawa et al merely considered the effects from the user and obstacles, and their robot could only follow the user.…”

Section: Introductionmentioning

confidence: 99%

“…We propose generating special artificial potential fields for the users and goal separately, and we integrate them with the basic potential fields generated from the other obstacles. The work of Nakazawa et al merely considered the effects from the user and obstacles, and their robot could only follow the user. By contrast, our method considers the status of the users and goal separately to influence the motion of the robot.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A framework for adaptive motion control of autonomous sociable guide robot

Zhang

Nagaoka

Kaneko

2016

IEEJ Transactions Elec Engng

Self Cite

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We present a framework by which the motion of an autonomous mobile guide robot is adaptively controlled. A sociable robot should adapt its speed and path to suit the users' activities, without restricting the user movement. By generating adaptive artificial potential fields for the users and the subgoal separately, and integrating them with the basic potential fields generated from obstacles, our robot can adapt to the users' activities and provide sociable tour-guide services. The robot predicts a user's moving speed and adapts to it to maintain the social distance. Moreover, with the proposed framework, users can deviate from the guided path temporarily and return to the original task afterward. Instead of waiting for the users and taking the risk of losing them, the robot deviates from its original path to follow the users and also prepares for returning to the guiding task. The robot restarts the guiding task at that place, which ensures the least cost to reach the goal. Simulation and experimental results show that our framework can automatically generate suitable motion patterns to control the robot adaptively, making it sociable while providing tour guide services.

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Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People

Repiso

Garrell

Sanfeliu

2019

Int J of Soc Robotics

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This paper presents a new framework for how autonomous social robots approach and accompany people in urban environments. The method discussed allows the robot to accompany a person and approach to other one, by adapting its own navigation in anticipation of future interactions with other people or contact with static obstacles. The contributions of the paper are manifold: firstly, we extended the Social Force model and the Anticipative Kinodynamic Planner [1] to the case of an adaptive side-by-side navigation; secondly, we enhance side-by-side navigation with an approaching task and a final positioning that allows the robot to interact with both people; and finally, we use findings from experiments of real-life observations of people walking in pairs to define the parameters of the human-robot interaction in our case of adaptive sideby-side. The method was validated by a large set of simulations; we also conducted real-life experiments with our robot, Tibi, to validate the framework described for the interaction process. In addition, we carried out various surveys and user studies to indicate the social acceptability of the robots performance of the accompanying, approaching and positioning tasks.

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Movement Control of Accompanying Robot Based on Artificial Potential Field Adapted to Dynamic Environments

Cited by 4 publications

References 7 publications

Human-Robot Collaborative Navigation Search Using Social Reward Sources

Human-Robot Collaborative Navigation Search Using Social Reward Sources

A framework for adaptive motion control of autonomous sociable guide robot

Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People

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