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

Nakazawa, Kazushi; Takahashi, Keita; Kaneko, Masahide

doi:10.1002/eej.22724

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…To enhance the adaptivity in path selection, Pandey and Alami [4] planned the trajectory via a multi-variant Gaussian model and switched between different guiding modes for corresponding human behaviors. Nakazawa et al [5] and Zhang et al [6] tackled the path planning problem based on artificial potential field, realizing more natural mode transitions with one unified framework. To improve robot adaptivity in migration velocity, a speed adjustment strategy [2] was proposed based on hierarchical Mixed Observability Markov Decision Process (MOMDP) for high-level decision making.…”

Section: A Robot Guidementioning

confidence: 99%

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A Multi-Behavior Planning Framework for Robot Guide

Hou¹,

Mu²,

Li³

et al. 2022

Preprint

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The guiding task of a mobile robot requires not only human-aware navigation, but also appropriate yet timely interaction for active instruction. State-of-the-art tour-guide models limit their socially-aware consideration to adapting to users' motion, ignoring the interactive behavior planning to fulfill the communicative demands. We propose a multi-behavior planning framework based on Monte Carlo Tree Search to better assist users to understand confusing scene contexts, select proper paths and timely arrive at the destination. To provide proactive guidance, we construct a sampling-based probability model of human motion to consider the interrelated effects between robots and humans. We validate our method both in simulation and real-world experiments along with performance comparison with state-of-the-art models.

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Section: A Robot Guidementioning

confidence: 99%

“…Sampling-based Prediction Algorithm With the pre-solved optimal state values V * g via (3) for each MDP setup of candidate goals g ∈ G, the probability distribution of the goals can be obtained via (5). For each sample, we randomly choose a goal conforming to its possibility and follow it Fig.…”

Section: B Human Motion Predictionmentioning

confidence: 99%

A Multi-Behavior Planning Framework for Robot Guide

Hou¹,

Mu²,

Li³

et al. 2022

Preprint

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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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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 9 publications

References 8 publications

A Multi-Behavior Planning Framework for Robot Guide

A Multi-Behavior Planning Framework for Robot Guide

Human-Robot Collaborative Navigation Search Using Social Reward Sources

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

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