add publications

Author: RanulfoB

bibs/inproceedings/10-1109-icar-46387-2019-8981587/cite.bib

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+@article{10.1109/icar46387.2019.8981587,
+ abstract = {One of the major challenges that autonomous cars are facing today is the unpredictability of pedestrian movement in urban environments. Since pedestrian data acquired by vehicles are sparse observed a pedestrian flow directed graph is proposed to understand pedestrian behavior. In this work, an autonomous electric vehicle is employed to gather LiDAR and camera data. Pedestrian tracking information and semantic information from the environment are used with a probabilistic approach to create the graph. In order to refine the graph a set of outlier removal techniques are described. The graph-based pedestrian flow shows an increase of 61.29 % of coverage zone, and the outlier removal approach successfully removed 81 % of the edges.},
+ author = {Bezerra, Ranulfo and Ohno, Kazunori and Westfechtel, Thomas and Tadokoro, Satoshi},
+ doi = {10.1109/icar46387.2019.8981587},
+ journal = {2019 19th International Conference on Advanced Robotics (ICAR)},
+ keywords = {},
+ pages = {779--784},
+ title = {Pedestrian Flow Estimation Using Sparse Observation for Autonomous Vehicles},
+ volume = {00},
+ year = {2019}
+}
+

bibs/inproceedings/10-1109-icar-46387-2019-8981587/index.md

@@ -0,0 +1,13 @@
+---
+title: "Pedestrian Flow Estimation Using Sparse Observation for Autonomous Vehicles"
+date: 2019-01-01
+publishDate: 2022-07-15T16:47:25.659252Z
+authors: ["Ranulfo Bezerra", "Kazunori Ohno", "Thomas Westfechtel", "Satoshi Tadokoro"]
+publication_types: ["2"]
+abstract: "One of the major challenges that autonomous cars are facing today is the unpredictability of pedestrian movement in urban environments. Since pedestrian data acquired by vehicles are sparse observed a pedestrian flow directed graph is proposed to understand pedestrian behavior. In this work, an autonomous electric vehicle is employed to gather LiDAR and camera data. Pedestrian tracking information and semantic information from the environment are used with a probabilistic approach to create the graph. In order to refine the graph a set of outlier removal techniques are described. The graph-based pedestrian flow shows an increase of 61.29 % of coverage zone, and the outlier removal approach successfully removed 81 % of the edges."
+featured: false
+publication: "*2019 19th International Conference on Advanced Robotics (ICAR)*"
+tags: [""]
+doi: "10.1109/icar46387.2019.8981587"
+---
+

bibs/inproceedings/10-1109-iros-45743-2020-9340697/cite.bib

@@ -0,0 +1,12 @@
+@article{10.1109/iros45743.2020.9340697,
+ abstract = {Backhoe loads sediment onto the bed of dump trucks during earthmoving work. The prediction of backhoe loading time is essential for ensuring safe cooperation between the backhoe and dump trucks. However, it is difficult to predict the instant at which the backhoe is ready to load sediment, because of the similarity in motions observed during gathering sediment. Moreover, since operators have different skill levels, the prediction requires a unique model for each operator. In this study, we attempt to predict the instant at which the backhoe is ready to load sediment into the dump truck. For this purpose, the beta-process hidden Markov model (BP-HMM) is employed to build a backhoe motion model for a specific operator. Time series data of backhoe loading motions for crushed rocks and wood chips, which were measured using 6-axis inertial measurement unit (IMU) sensors equipped at the cab, boom, and arm of the backhoe, were used for modeling with the BP-HMM. Several primitive motions of the backhoe, which occur at the completion of preparation before the loading process begins, were discovered as a result of the motion modeling based on the BP-HMM. We developed the prediction of the instant using three primitive motions. At best, the proposed method could predict the instant with a probability of 67% and 100%, at 6.0 s and 0.7 s before the loading motions began, respectively. This phased prediction can be used to reduce the idle time and risk for dump trucks during earthmoving work with the backhoe.},
+ author = {Yamada, Kento and Ohno, Kazunori and Hamada, Ryunosuke and Westfechtel, Thomas and Bezerra, Ranulfo and Miyamoto, Naoto and Suzuki, Taro and Suzuki, Takahiro and Nagatani, Keiji and Shibata, Yukinori and Asano, Kimitaka and Komatsu, Tomohiro and Tadokoro, Satoshi},
+ doi = {10.1109/iros45743.2020.9340697},
+ journal = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+ keywords = {},
+ pages = {2663--2670},
+ title = {Prediction of Backhoe Loading Motion via the Beta-Process Hidden Markov Model},
+ volume = {00},
+ year = {2021}
+}
+

bibs/inproceedings/10-1109-iros-45743-2020-9340697/index.md

@@ -0,0 +1,13 @@
+---
+title: "Prediction of Backhoe Loading Motion via the Beta-Process Hidden Markov Model"
+date: 2021-01-01
+publishDate: 2022-07-15T16:47:25.650730Z
+authors: ["Kento Yamada", "Kazunori Ohno", "Ryunosuke Hamada", "Thomas Westfechtel", "Ranulfo Bezerra", "Naoto Miyamoto", "Taro Suzuki", "Takahiro Suzuki", "Keiji Nagatani", "Yukinori Shibata", "Kimitaka Asano", "Tomohiro Komatsu", "Satoshi Tadokoro"]
+publication_types: ["2"]
+abstract: "Backhoe loads sediment onto the bed of dump trucks during earthmoving work. The prediction of backhoe loading time is essential for ensuring safe cooperation between the backhoe and dump trucks. However, it is difficult to predict the instant at which the backhoe is ready to load sediment, because of the similarity in motions observed during gathering sediment. Moreover, since operators have different skill levels, the prediction requires a unique model for each operator. In this study, we attempt to predict the instant at which the backhoe is ready to load sediment into the dump truck. For this purpose, the beta-process hidden Markov model (BP-HMM) is employed to build a backhoe motion model for a specific operator. Time series data of backhoe loading motions for crushed rocks and wood chips, which were measured using 6-axis inertial measurement unit (IMU) sensors equipped at the cab, boom, and arm of the backhoe, were used for modeling with the BP-HMM. Several primitive motions of the backhoe, which occur at the completion of preparation before the loading process begins, were discovered as a result of the motion modeling based on the BP-HMM. We developed the prediction of the instant using three primitive motions. At best, the proposed method could predict the instant with a probability of 67% and 100%, at 6.0 s and 0.7 s before the loading motions began, respectively. This phased prediction can be used to reduce the idle time and risk for dump trucks during earthmoving work with the backhoe."
+featured: false
+publication: "*2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*"
+tags: [""]
+doi: "10.1109/iros45743.2020.9340697"
+---
+

bibs/inproceedings/10-1109-itsc-48978-2021-9564906/cite.bib

@@ -0,0 +1,12 @@
+@article{10.1109/itsc48978.2021.9564906,
+ abstract = {Automated Driving Systems (ADSs) are being proposed as a promising technology that will help drivers avoid accidents, as well as reduce driving-related stress. To enhance the driving performance of ADSs, driving log data from human drivers is being used to teach these systems how to behave in different traffic regions. Visual information from the driving log data is an easy method to identify traffic regions. However, due to space constraints or lack of visual sensors, most of the driving log data has no visual information. Therefore, another solution is necessary to identify traffic regions using only spatial information. To address this challenge, the paper proposes a region recognition method using key primitive transitions obtained from vehicle trajectory information. To obtain the motion primitives, we employed a hierarchical similarity clustering that combines hierarchical clustering and HDP-HSMM. The vehicle behavior from the region of interest was extracted by analyzing the motion primitives transitions located within close range (50 m) of the region. We assessed the behavior of vehicles when approaching two regions: traffic lights and entrances. Three environments were used to evaluate the proposed method, with two different drivers and distinguished layouts. This study shows that the proposed classification method can identify traffic light and entrance regions with an average 89% precision and 86% f-score. Additionally, the hierarchical similarity clustering and window observation approach developed in this study is responsible for increasing the system's precision by 9%.},
+ author = {Bezerra, Ranulfo and Ohno, Kazunori and Kojima, Shotaro and Tadokoro, Satoshi},
+ doi = {10.1109/itsc48978.2021.9564906},
+ journal = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},
+ keywords = {},
+ pages = {1437--1444},
+ title = {Region Recognition Based on HMM Using Primitive Motion Transitions},
+ volume = {00},
+ year = {2021}
+}
+

bibs/inproceedings/10-1109-itsc-48978-2021-9564906/index.md

@@ -0,0 +1,13 @@
+---
+title: "Region Recognition Based on HMM Using Primitive Motion Transitions"
+date: 2021-01-01
+publishDate: 2022-07-15T16:47:25.650924Z
+authors: ["Ranulfo Bezerra", "Kazunori Ohno", "Shotaro Kojima", "Satoshi Tadokoro"]
+publication_types: ["2"]
+abstract: "Automated Driving Systems (ADSs) are being proposed as a promising technology that will help drivers avoid accidents, as well as reduce driving-related stress. To enhance the driving performance of ADSs, driving log data from human drivers is being used to teach these systems how to behave in different traffic regions. Visual information from the driving log data is an easy method to identify traffic regions. However, due to space constraints or lack of visual sensors, most of the driving log data has no visual information. Therefore, another solution is necessary to identify traffic regions using only spatial information. To address this challenge, the paper proposes a region recognition method using key primitive transitions obtained from vehicle trajectory information. To obtain the motion primitives, we employed a hierarchical similarity clustering that combines hierarchical clustering and HDP-HSMM. The vehicle behavior from the region of interest was extracted by analyzing the motion primitives transitions located within close range (50 m) of the region. We assessed the behavior of vehicles when approaching two regions: traffic lights and entrances. Three environments were used to evaluate the proposed method, with two different drivers and distinguished layouts. This study shows that the proposed classification method can identify traffic light and entrance regions with an average 89% precision and 86% f-score. Additionally, the hierarchical similarity clustering and window observation approach developed in this study is responsible for increasing the system's precision by 9%."
+featured: false
+publication: "*2021 IEEE International Intelligent Transportation Systems Conference (ITSC)*"
+tags: [""]
+doi: "10.1109/itsc48978.2021.9564906"
+---
+

bibs/inproceedings/10-1109-lra-2021-3062606/cite.bib

@@ -0,0 +1,14 @@
+@article{10.1109/lra.2021.3062606,
+ abstract = {Semantic maps are an important tool to provide robots with high-level knowledge about the environment, enabling them to better react to and interact with their surroundings. However, as a single measurement of the environment is solely a snapshot of a specific time, it does not necessarily reflect the underlying semantics. In this work, we propose a method to create a semantic map of a construction site by fusing multiple daily data. The construction site is measured by an unmanned aerial vehicle (UAV) equipped with a LiDAR. We extract clusters above ground level from the measurements and classify them using either a random forest or a deep learning based classifier. Furthermore, we combine the classification results of several measurements to generalize the classification of the single measurements and create a general semantic map of the working site. We measured two construction fields for our evaluation. The classification models can achieve an average intersection over union (IoU) score of 69.2 during classification on the Sanbongi field, which is used for training, validation and testing and an IoU score of 49.16 on a hold-out testing field. In a final step, we show how the semantic map can be employed to suggest a parking spot for a dump truck, and in addition, show that the semantic map can be utilized to improve path planning inside the construction site.},
+ author = {Westfechtel, Thomas and Ohno, Kazunori and Akegawa, Tetsu and Yamada, Kento and Bezerra, Ranulfo and Kojima, Shotaro and Suzuki, Taro and Komatsu, Tomohiro and Shibata, Yukinori and Asano, Kimitaka and Nagatani, Keji and Miyamoto, Naoto and Suzuki, Takahiro and Harada, Tatsuya and Tadokoro, Satoshi},
+ doi = {10.1109/lra.2021.3062606},
+ issn = {2377-3766},
+ journal = {IEEE Robotics and Automation Letters},
+ keywords = {},
+ number = {2},
+ pages = {3073--3080},
+ title = {Semantic Mapping of Construction Site From Multiple Daily Airborne LiDAR Data},
+ volume = {6},
+ year = {2020}
+}
+

bibs/inproceedings/10-1109-lra-2021-3062606/index.md

@@ -0,0 +1,13 @@
+---
+title: "Semantic Mapping of Construction Site From Multiple Daily Airborne LiDAR Data"
+date: 2020-01-01
+publishDate: 2022-07-15T16:47:25.658821Z
+authors: ["Thomas Westfechtel", "Kazunori Ohno", "Tetsu Akegawa", "Kento Yamada", "Ranulfo Bezerra", "Shotaro Kojima", "Taro Suzuki", "Tomohiro Komatsu", "Yukinori Shibata", "Kimitaka Asano", "Keji Nagatani", "Naoto Miyamoto", "Takahiro Suzuki", "Tatsuya Harada", "Satoshi Tadokoro"]
+publication_types: ["2"]
+abstract: "Semantic maps are an important tool to provide robots with high-level knowledge about the environment, enabling them to better react to and interact with their surroundings. However, as a single measurement of the environment is solely a snapshot of a specific time, it does not necessarily reflect the underlying semantics. In this work, we propose a method to create a semantic map of a construction site by fusing multiple daily data. The construction site is measured by an unmanned aerial vehicle (UAV) equipped with a LiDAR. We extract clusters above ground level from the measurements and classify them using either a random forest or a deep learning based classifier. Furthermore, we combine the classification results of several measurements to generalize the classification of the single measurements and create a general semantic map of the working site. We measured two construction fields for our evaluation. The classification models can achieve an average intersection over union (IoU) score of 69.2 during classification on the Sanbongi field, which is used for training, validation and testing and an IoU score of 49.16 on a hold-out testing field. In a final step, we show how the semantic map can be employed to suggest a parking spot for a dump truck, and in addition, show that the semantic map can be utilized to improve path planning inside the construction site."
+featured: false
+publication: "*IEEE Robotics and Automation Letters*"
+tags: [""]
+doi: "10.1109/lra.2021.3062606"
+---
+

bibs/inproceedings/10-1109-sii-46433-2020-9026227/cite.bib

@@ -0,0 +1,12 @@
+@article{10.1109/sii46433.2020.9026227,
+ abstract = {Sensing position and orientation of construction vehicle is an important issue for automation of construction process. We aim to develop sensing and visualization technologies for construction vehicles. Our target construction vehicle is backhoes. Construction vehicles are usually rented in construction fields. However, construction vehicles that can be rented do not have the sensing function. It is too hard to obtain backhoe position and manipulator pose without sensing information. This paper proposes an attachable sensor box that can measure backhoe position and orientation and the manipulator pose. The sensor boxes can be attached on backhoe metal surface by magnetic force without any additional manufacturing on the construction vehicle surface. By using WiFi communication and mobile battery the sensor box can be easily attached on large-size backhoe without wiring. After the work is done, it is easily detached by changing magnetic force power. At loading and scooping motion, backhoe arm and boom have large force exceeding 16 G. To attach the sensor boxes to the arm and boom, we designed an additional magnetic frame that can generate force of 1960 N. The sensor boxes were firmly attached to each joint and prevented to drop by any force from backhoes movement. We also measured the behavior of the backhoe in loading works in hot conditions over 30 °C and visualized backhoe pose and tip manipulator position.},
+ author = {Yamada, Kento and Ohno, Kazunori and Miyamoto, Naoto and Suzuki, Taro and Kojima, Shotaro and Bezerra, Ranulfo and Suzuki, Takahiro and Nagatani, Keiji and Shibata, Yukinori and Asano, Kimitaka and Komatsu, Tomohiro and Tadokoro, Satoshi},
+ doi = {10.1109/sii46433.2020.9026227},
+ journal = {2020 IEEE/SICE International Symposium on System Integration (SII)},
+ keywords = {},
+ pages = {706--711},
+ title = {Attachable Sensor Boxes to Visualize Backhoe Motion},
+ volume = {00},
+ year = {2020}
+}
+

bibs/inproceedings/10-1109-sii-46433-2020-9026227/index.md

@@ -0,0 +1,13 @@
+---
+title: "Attachable Sensor Boxes to Visualize Backhoe Motion"
+date: 2020-01-01
+publishDate: 2022-07-15T16:47:25.651082Z
+authors: ["Kento Yamada", "Kazunori Ohno", "Naoto Miyamoto", "Taro Suzuki", "Shotaro Kojima", "Ranulfo Bezerra", "Takahiro Suzuki", "Keiji Nagatani", "Yukinori Shibata", "Kimitaka Asano", "Tomohiro Komatsu", "Satoshi Tadokoro"]
+publication_types: ["2"]
+abstract: "Sensing position and orientation of construction vehicle is an important issue for automation of construction process. We aim to develop sensing and visualization technologies for construction vehicles. Our target construction vehicle is backhoes. Construction vehicles are usually rented in construction fields. However, construction vehicles that can be rented do not have the sensing function. It is too hard to obtain backhoe position and manipulator pose without sensing information. This paper proposes an attachable sensor box that can measure backhoe position and orientation and the manipulator pose. The sensor boxes can be attached on backhoe metal surface by magnetic force without any additional manufacturing on the construction vehicle surface. By using WiFi communication and mobile battery the sensor box can be easily attached on large-size backhoe without wiring. After the work is done, it is easily detached by changing magnetic force power. At loading and scooping motion, backhoe arm and boom have large force exceeding 16 G. To attach the sensor boxes to the arm and boom, we designed an additional magnetic frame that can generate force of 1960 N. The sensor boxes were firmly attached to each joint and prevented to drop by any force from backhoes movement. We also measured the behavior of the backhoe in loading works in hot conditions over 30 °C and visualized backhoe pose and tip manipulator position."
+featured: false
+publication: "*2020 IEEE/SICE International Symposium on System Integration (SII)*"
+tags: [""]
+doi: "10.1109/sii46433.2020.9026227"
+---
+

bibs/inproceedings/10-5753-cbie-sbie-2017-907/cite.bib

@@ -0,0 +1,10 @@
+@article{10.5753/cbie.sbie.2017.907,
+ author = {Rocha, Francisco Bruno and Rocha, Diego Porto and Monção, Nayana and Bezerra, Ranulfo and Costa, João Guilherme Cavalcanti and Farias, Karoline De Moura and Lima, Bruno Vicente and Santana, Andre Macedo},
+ doi = {10.5753/cbie.sbie.2017.907},
+ journal = {Anais do XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017)},
+ keywords = {},
+ pages = {907},
+ title = {AbaQuim - Um Jogo Educativo para Auxílio na Aprendizagem de Distribuição Eletrônica Química},
+ year = {2017}
+}
+

bibs/inproceedings/10-5753-cbie-sbie-2017-907/index.md

@@ -0,0 +1,13 @@
+---
+title: "AbaQuim - Um Jogo Educativo para Auxílio na Aprendizagem de Distribuição Eletrônica Química"
+date: 2017-01-01
+publishDate: 2022-07-15T16:47:25.659987Z
+authors: ["Francisco Bruno Rocha", "Diego Porto Rocha", "Nayana Monção", "Ranulfo Bezerra", "João Guilherme Cavalcanti Costa", "Karoline De Moura Farias", "Bruno Vicente Lima", "Andre Macedo Santana"]
+publication_types: ["2"]
+abstract: ""
+featured: false
+publication: "*Anais do XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017)*"
+tags: [""]
+doi: "10.5753/cbie.sbie.2017.907"
+---
+