Localization and Navigation for Autonomous Mobile Robots Using Petri Nets in Indoor Environments

Mota, Francisco Alan Xavier da; Rocha, Matheus Xavier; Rodrigues, Joel J. P. C.; Albuquerque, Victor Hugo C. de; Alexandria, Auzuir Ripardo de

doi:10.1109/access.2018.2846554

Cited by 54 publications

(26 citation statements)

References 27 publications

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“…There are many ways to measure human and environmental information, such as a stereo camera, depth camera, laser range finder (LRF), and global positioning system (GPS). Moreover, there are many ways to understand scenes such as simultaneously localization and mapping (SLAM) using petri nets [52] and considering relocation performance [53].…”

Section: Pre-processing: Scene Classificationmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human-robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans' psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

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Section: Pre-processing: Scene Classificationmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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“…In (2) there was vertical support of the motor (3) and it was connected to a propeller (4). At the rear (stern), there was a servo motor (5) connected to the rudder (6). Further, from the stern comes the data cable (7) and a mini float (8), connecting the probes (9).…”

Section: Vector: the Boatmentioning

confidence: 99%

Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes

et al. 2019

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Maintenance of water resources through collection of water followed by laboratory analysis, is a key factor in the measurement of water quality. The main difficulty for water collection and analysis is the logistics of the process, since the collections are often made by mall boats in very distant aquifers, applying manual processes, and are sometimes based on few samples. In this paper, the development, construction, and implementation of a robotic airboat to measure water quality in lakes has been described. The airboat was developed in the form of a mini-boat, in a fiberglass structure, whose interior housed a battery, a Raspberry Pi mini-computer, a Wi-Fi router for connection to a notebook, tablet or cell phone, and sensors connected to the Arduino platform. The airboat was validated in two lagoons in Fortaleza, and in a reservoir in the city of Pacatuba. The results were collected with the purpose of analyzing the parameters of each lagoon. The main contribution of this work was the development of an autonomous system of acquiring water parameters from several points of the lagoon.

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“…While in [5], a probabilistic model is used to identify scenes and objects, and a hierarchical model to process the robot's location. In [4], radio-frequency identification (RFID) is used to locate a mobile robot in an indoor environment, and incidences matrices as map of the paths. Futhermore, [6] proposed a Recurrent Convolutional Neural Network (RCNN) model that uses the fusion of a 2D laser signal with an inertial sensor signal to train and improve prediction scenarios.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Mobile Robots Localization based on Omnidirectional Sonar Images and Machine Learning

Filho¹,

Silva²,

Ohata³

et al. 2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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In later years, research in mobile robotic areas have been experiencing a growth in interest due to its vast application area. In an unknown environment, the robot's location and movement are essential for its operation. In addition, machine learning techniques, along with signal or image processing, have been applied to map the environment, locate and move the mobile robot. This article proposes a low cost and efficient approach for mobile robot localization. It uses a omnidirectional sonar with machine learning and image processing. The feature extractors used in this paper were: Structural Co-occurrence Matrix (SCM), Statistical Moments, Central Moments, Hu Moments and Gray Level Co-occurrence Matrix (GLCM). The classifiers used in this study were: Bayes classifier, k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Optimum Path Forest (OPF) and Support Vector Machines (SVM). The results showed that the best accuracy was achieved with Central Moments as feature extractor and OPF as classifier, achieving 96.61% and with a test time of 100us.

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Localization and Navigation for Autonomous Mobile Robots Using Petri Nets in Indoor Environments

Cited by 54 publications

References 27 publications

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes

A New Strategy for Mobile Robots Localization based on Omnidirectional Sonar Images and Machine Learning

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