A graph neural network to model disruption in human-aware robot navigation

Bachiller, Pilar; Rodriguez-Criado, Daniel; Jorvekar, Ronit R.; Bustos, Pablo; Faria, Diego R.; Manso, Luis J.

doi:10.1007/s11042-021-11113-6

Cited by 19 publications

(6 citation statements)

References 37 publications

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“…To decide the external parameters (hyper-parameters) such as learning rate and number of hidden layer neurons is a difficult task and can be solved by Bayesian search. The Bayesian search is the optimization technique of Machine Learning (ML) (Bachiller et al 2021 ) that can find the optimum hyper-parameters (learning rate and number of hidden layer neurons) of Feed-forward Neural Network (FFNN). So self-sensing of Shape memory coil by ML can be the suitable alternative to the mentioned problem (Kaur and Kadam 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Machine learning-based self-sensing of the stiffness of shape memory coil actuator

Sul

Dhanalakshami

2022

Soft Comput

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Self-sensing actuation (SSA) assists in sensing the vital property of the shape memory coil which can be used to monitor and control the actuation in smart equipment as well as robotics. The stiffness characteristic of the shape memory coil (SMC) is sensed during actuation which plays a significant role in development of intelligent robotics in defense systems. The electrical property of SMC such as electrical resistance changes due to martensitic phase transformation which is further used to sense the mechanical properties such as strain, stress, temperature, length, and force of the equipment. Nowadays electrical properties are used to sense the stiffness of the shape memory coil. As of now, there is no well-established analytical model to predict the stiffness of SMC during actuation accurately. Therefore, a soft model based on machine learning is proposed in this paper for autosensing of the stiffness in SMC. The experimental facility has been developed for the collection of data with respect to diverse Joule heating currents. To determine the experimental data values of stiffness and electrical resistance of SMC is a cumbersome task. Hence, we have proposed an automated method to predict the stiffness of the SMC using soft computing-based methods. The Classical Polynomial and Bayesian optimization-based Feedforward Neural Network (FFNN) models are developed for analyzing the stiffness of the SMC. It is found that the hybrid FFNN model outperforms the other ML-based model by attaining 95.2650% accuracy. The FFNN model is also able to explain almost all the predicted stiffness values which are experimentally recorded.

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Section: Introductionmentioning

confidence: 99%

“…In this research work, COVID-19 cases prediction is done by using piecewise regression (Senapati et al February 2021 ). The Random Forest and Support Vector Machine (SVM) algorithm are used to classify the susceptibility score of Individual toward COVID-19 infection (Bachiller et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Machine learning-based self-sensing of the stiffness of shape memory coil actuator

Sul

Dhanalakshami

2022

Soft Comput

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“…Alternatively, SocNav1 [360] and SocNav2 [349] datasets were designed to learn and benchmark functions estimating social conventions in robot navigation by using human feedback in simulated environments. Wang et al [361] developed TBD dataset containing human-verified labels, a combination of top-down and egocentric views, and naturalistic human behavior in the presence of a mobile capturing system moving in a socially acceptable way.…”

Section: Datasetsmentioning

confidence: 99%

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Karwowski,

Szynkiewicz,

Niewiadomska-Szynkiewicz

2024

Sensors

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Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

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“…owing to its importance, numerous studies have focused on this level of autonomous vehicles' operation, and a wide range of tools and methods have been applied by researchers, from model predictive control (MPC) to deep learning-based algorithms, and end-to-end frameworks [138], [139]. Although GNNs have been recently used in the motion planning of in-door robots and unmanned aerial vehicles (UAVs) and have shown superior performance [140], [141], [142], their application in the motion planning of autonomous vehicles in the real-world situations has been yet relatively limited. In the domain of the autonomous vehicle, Hugle et al [143] proposed Graph-Q for the control of autonomous vehicles in urban and multi-agent scenarios by considering the interactions among different vehicles in the scene in form of a graph.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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A graph neural network to model disruption in human-aware robot navigation

Cited by 19 publications

References 37 publications

RETRACTED ARTICLE: Machine learning-based self-sensing of the stiffness of shape memory coil actuator

RETRACTED ARTICLE: Machine learning-based self-sensing of the stiffness of shape memory coil actuator

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Graph Neural Networks for Intelligent Transportation Systems: A Survey

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