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You searched for +publisher:"Rutgers University" +contributor:("Yu, Jingjin"). Showing records 1 – 15 of 15 total matches.

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Rutgers University

1. Chakraborty, Nilay, 1989-. Multiplayer game frameworks for crowd-aware co-design of environment.

Degree: MS, Computer Science, 2017, Rutgers University

 This paper proposes two frameworks in the field of Games in Research. • Proposal1 Architectural design decisions stand to benefit by accounting for the presence… (more)

Subjects/Keywords: Architectural design; Video games – Design

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APA (6th Edition):

Chakraborty, Nilay, 1. (2017). Multiplayer game frameworks for crowd-aware co-design of environment. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/53469/

Chicago Manual of Style (16th Edition):

Chakraborty, Nilay, 1989-. “Multiplayer game frameworks for crowd-aware co-design of environment.” 2017. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/53469/.

MLA Handbook (7th Edition):

Chakraborty, Nilay, 1989-. “Multiplayer game frameworks for crowd-aware co-design of environment.” 2017. Web. 31 Oct 2020.

Vancouver:

Chakraborty, Nilay 1. Multiplayer game frameworks for crowd-aware co-design of environment. [Internet] [Masters thesis]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53469/.

Council of Science Editors:

Chakraborty, Nilay 1. Multiplayer game frameworks for crowd-aware co-design of environment. [Masters Thesis]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53469/


Rutgers University

2. Xie, Xiaoyang, 1988-. Fleet-oriented real-time vehicular tracking at urban scale.

Degree: MS, Computer Science, 2017, Rutgers University

 Nowadays, vehicular sensing has become increasingly important to collect urban data to understand and address mobility challenges. A straightforward way to achieve this goal is… (more)

Subjects/Keywords: Intelligent transportation systems

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APA (6th Edition):

Xie, Xiaoyang, 1. (2017). Fleet-oriented real-time vehicular tracking at urban scale. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/54045/

Chicago Manual of Style (16th Edition):

Xie, Xiaoyang, 1988-. “Fleet-oriented real-time vehicular tracking at urban scale.” 2017. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/54045/.

MLA Handbook (7th Edition):

Xie, Xiaoyang, 1988-. “Fleet-oriented real-time vehicular tracking at urban scale.” 2017. Web. 31 Oct 2020.

Vancouver:

Xie, Xiaoyang 1. Fleet-oriented real-time vehicular tracking at urban scale. [Internet] [Masters thesis]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54045/.

Council of Science Editors:

Xie, Xiaoyang 1. Fleet-oriented real-time vehicular tracking at urban scale. [Masters Thesis]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54045/


Rutgers University

3. Yang, Yu, 1994-. Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing.

Degree: MS, Computer Science, 2017, Rutgers University

 Real-time mobility is important for many real-world applications, e.g., transportation, urban planning given different level administrative jurisdiction. However, most of the existing work focuses at… (more)

Subjects/Keywords: Vehicular ad hoc networks (Computer networks); Intelligent transportation systems

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APA (6th Edition):

Yang, Yu, 1. (2017). Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/54051/

Chicago Manual of Style (16th Edition):

Yang, Yu, 1994-. “Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing.” 2017. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/54051/.

MLA Handbook (7th Edition):

Yang, Yu, 1994-. “Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing.” 2017. Web. 31 Oct 2020.

Vancouver:

Yang, Yu 1. Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing. [Internet] [Masters thesis]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54051/.

Council of Science Editors:

Yang, Yu 1. Vehicular mobility modeling on a large scale: an approach to combine stationary sensing and mobile sensing. [Masters Thesis]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54051/


Rutgers University

4. Psarakis, Zacharias, 1989-. Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics.

Degree: MS, Computer Science, 2018, Rutgers University

 The Amazon Robotics Challenge was an event created by Amazon to bring robotics teams together and try to push forward the research on robotics automation… (more)

Subjects/Keywords: Warehouses – Management; Robots – Programming

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APA (6th Edition):

Psarakis, Zacharias, 1. (2018). Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/57682/

Chicago Manual of Style (16th Edition):

Psarakis, Zacharias, 1989-. “Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics.” 2018. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/57682/.

MLA Handbook (7th Edition):

Psarakis, Zacharias, 1989-. “Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics.” 2018. Web. 31 Oct 2020.

Vancouver:

Psarakis, Zacharias 1. Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics. [Internet] [Masters thesis]. Rutgers University; 2018. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57682/.

Council of Science Editors:

Psarakis, Zacharias 1. Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics. [Masters Thesis]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57682/


Rutgers University

5. Ren, Baozhang. Development of an efficient RGB-D annotation tool for video sequence.

Degree: MS, Computer Science, 2020, Rutgers University

 With the prevalence of neural networks and deep learning models, more data is required to expand the domain as well as to improve the accuracy… (more)

Subjects/Keywords: RGBD; Image processing  – Digital techniques

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APA (6th Edition):

Ren, B. (2020). Development of an efficient RGB-D annotation tool for video sequence. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62750/

Chicago Manual of Style (16th Edition):

Ren, Baozhang. “Development of an efficient RGB-D annotation tool for video sequence.” 2020. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62750/.

MLA Handbook (7th Edition):

Ren, Baozhang. “Development of an efficient RGB-D annotation tool for video sequence.” 2020. Web. 31 Oct 2020.

Vancouver:

Ren B. Development of an efficient RGB-D annotation tool for video sequence. [Internet] [Masters thesis]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62750/.

Council of Science Editors:

Ren B. Development of an efficient RGB-D annotation tool for video sequence. [Masters Thesis]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62750/


Rutgers University

6. Liu, Jingjing, 1985-. Exploiting multispectral and contextual information to improve human detection.

Degree: PhD, Computer Science, 2017, Rutgers University

Human detection has various applications, e.g., autonomous driving car, surveillance system, and retail. In this dissertation, we first exploit multispectral images (i.e., RGB and thermal… (more)

Subjects/Keywords: Robotics – Human factors; Human-robot interaction

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APA (6th Edition):

Liu, Jingjing, 1. (2017). Exploiting multispectral and contextual information to improve human detection. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/55564/

Chicago Manual of Style (16th Edition):

Liu, Jingjing, 1985-. “Exploiting multispectral and contextual information to improve human detection.” 2017. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/55564/.

MLA Handbook (7th Edition):

Liu, Jingjing, 1985-. “Exploiting multispectral and contextual information to improve human detection.” 2017. Web. 31 Oct 2020.

Vancouver:

Liu, Jingjing 1. Exploiting multispectral and contextual information to improve human detection. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55564/.

Council of Science Editors:

Liu, Jingjing 1. Exploiting multispectral and contextual information to improve human detection. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55564/


Rutgers University

7. Peng, Xi, 1986-. Learning disentangled representations in deep visual analysis.

Degree: PhD, Computer Science, 2018, Rutgers University

Learning reliable and interpretable representations is one of the fundamental challenges in machine learning and computer vision. Over the last decade, deep neural networks have… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Peng, Xi, 1. (2018). Learning disentangled representations in deep visual analysis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/56078/

Chicago Manual of Style (16th Edition):

Peng, Xi, 1986-. “Learning disentangled representations in deep visual analysis.” 2018. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/56078/.

MLA Handbook (7th Edition):

Peng, Xi, 1986-. “Learning disentangled representations in deep visual analysis.” 2018. Web. 31 Oct 2020.

Vancouver:

Peng, Xi 1. Learning disentangled representations in deep visual analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56078/.

Council of Science Editors:

Peng, Xi 1. Learning disentangled representations in deep visual analysis. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56078/


Rutgers University

8. Zhou, Kang. Artificial intelligence-aided prediction of broken rail-caused derailment risk.

Degree: PhD, Civil and Environmental Engineering, 2020, Rutgers University

 Broken rails are the leading cause of freight train derailments in the United States. The American railroad industry annually spends billions of dollars on track… (more)

Subjects/Keywords: Artificial intelligence-aided prediction; Railroad rails  – Maintenance and repair  – Computer simulation

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APA (6th Edition):

Zhou, K. (2020). Artificial intelligence-aided prediction of broken rail-caused derailment risk. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62621/

Chicago Manual of Style (16th Edition):

Zhou, Kang. “Artificial intelligence-aided prediction of broken rail-caused derailment risk.” 2020. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62621/.

MLA Handbook (7th Edition):

Zhou, Kang. “Artificial intelligence-aided prediction of broken rail-caused derailment risk.” 2020. Web. 31 Oct 2020.

Vancouver:

Zhou K. Artificial intelligence-aided prediction of broken rail-caused derailment risk. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62621/.

Council of Science Editors:

Zhou K. Artificial intelligence-aided prediction of broken rail-caused derailment risk. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62621/


Rutgers University

9. Tan, Chaowei, 1983-. Machine learning based image segmentation for large-scale osteoarthritis analysis.

Degree: PhD, Computer Science, 2020, Rutgers University

Osteoarthritis (OA) is the most common degenerative joint disease worldwide, tending to occur in the joints of hip and knee. Large adult population in the… (more)

Subjects/Keywords: Osteoarthritis analysis; Osteoarthritis  – Imaging

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APA (6th Edition):

Tan, Chaowei, 1. (2020). Machine learning based image segmentation for large-scale osteoarthritis analysis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62751/

Chicago Manual of Style (16th Edition):

Tan, Chaowei, 1983-. “Machine learning based image segmentation for large-scale osteoarthritis analysis.” 2020. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62751/.

MLA Handbook (7th Edition):

Tan, Chaowei, 1983-. “Machine learning based image segmentation for large-scale osteoarthritis analysis.” 2020. Web. 31 Oct 2020.

Vancouver:

Tan, Chaowei 1. Machine learning based image segmentation for large-scale osteoarthritis analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62751/.

Council of Science Editors:

Tan, Chaowei 1. Machine learning based image segmentation for large-scale osteoarthritis analysis. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62751/


Rutgers University

10. Yang, Zhixiong. Byzantine-resilient decentralized learning.

Degree: PhD, Electrical and Computer Engineering, 2020, Rutgers University

When datasets are distributed over a network and a central server is infeasible, machine learning has to be performed in a decentralized fashion. The dissertation… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Yang, Z. (2020). Byzantine-resilient decentralized learning. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62960/

Chicago Manual of Style (16th Edition):

Yang, Zhixiong. “Byzantine-resilient decentralized learning.” 2020. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62960/.

MLA Handbook (7th Edition):

Yang, Zhixiong. “Byzantine-resilient decentralized learning.” 2020. Web. 31 Oct 2020.

Vancouver:

Yang Z. Byzantine-resilient decentralized learning. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62960/.

Council of Science Editors:

Yang Z. Byzantine-resilient decentralized learning. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62960/


Rutgers University

11. Littlefield, Zakary, 1989-. Efficient and asymptotically optimal kinodynamic motion planning.

Degree: PhD, Robots  – Control systems, 2020, Rutgers University

This dissertation explores properties of motion planners that build tree data structures in a robot’s state space. Sampling-based tree planners are especially useful for planning… (more)

Subjects/Keywords: Computer Science

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APA (6th Edition):

Littlefield, Zakary, 1. (2020). Efficient and asymptotically optimal kinodynamic motion planning. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/64103/

Chicago Manual of Style (16th Edition):

Littlefield, Zakary, 1989-. “Efficient and asymptotically optimal kinodynamic motion planning.” 2020. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/64103/.

MLA Handbook (7th Edition):

Littlefield, Zakary, 1989-. “Efficient and asymptotically optimal kinodynamic motion planning.” 2020. Web. 31 Oct 2020.

Vancouver:

Littlefield, Zakary 1. Efficient and asymptotically optimal kinodynamic motion planning. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64103/.

Council of Science Editors:

Littlefield, Zakary 1. Efficient and asymptotically optimal kinodynamic motion planning. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64103/


Rutgers University

12. Shome, Rahul. The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees.

Degree: PhD, Robotics, 2020, Rutgers University

 This thesis deals with task and motion planning challenges, specifically those involving manipulating multiple objects using multiple robot manipulators. The contributions range from a new… (more)

Subjects/Keywords: Computer Science

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APA (6th Edition):

Shome, R. (2020). The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/64106/

Chicago Manual of Style (16th Edition):

Shome, Rahul. “The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees.” 2020. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/64106/.

MLA Handbook (7th Edition):

Shome, Rahul. “The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees.” 2020. Web. 31 Oct 2020.

Vancouver:

Shome R. The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64106/.

Council of Science Editors:

Shome R. The problem of many: efficient multi-arm, multi-object task and motion planning with optimality guarantees. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64106/

13. Kourtev, Hristiyan, 1982-. A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter.

Degree: MS, Computer Science, 2018, Rutgers University

 Robotic grasping has been an active area of research since the dawn of robotics. With recent advancements in artificial intelligence, vision, planning and machine learning,… (more)

Subjects/Keywords: Robotics; Soft computing

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APA (6th Edition):

Kourtev, Hristiyan, 1. (2018). A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/56044/

Chicago Manual of Style (16th Edition):

Kourtev, Hristiyan, 1982-. “A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter.” 2018. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/56044/.

MLA Handbook (7th Edition):

Kourtev, Hristiyan, 1982-. “A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter.” 2018. Web. 31 Oct 2020.

Vancouver:

Kourtev, Hristiyan 1. A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter. [Internet] [Masters thesis]. Rutgers University; 2018. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56044/.

Council of Science Editors:

Kourtev, Hristiyan 1. A robust soft and vacuum hybrid end-effector and compliant arm for picking in clutter. [Masters Thesis]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56044/

14. Dobson, Andrew, 1988-. Compact representations for efficient robot motion planning with formal guarantees.

Degree: PhD, Computer Science, 2017, Rutgers University

This work provides compact representations for single- and multi-robot motion planning in the context of prehensile robot manipulation. This work describes the asymptotic near-optimality and… (more)

Subjects/Keywords: Robots – Motion; Robots – Control systems

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APA (6th Edition):

Dobson, Andrew, 1. (2017). Compact representations for efficient robot motion planning with formal guarantees. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/55459/

Chicago Manual of Style (16th Edition):

Dobson, Andrew, 1988-. “Compact representations for efficient robot motion planning with formal guarantees.” 2017. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/55459/.

MLA Handbook (7th Edition):

Dobson, Andrew, 1988-. “Compact representations for efficient robot motion planning with formal guarantees.” 2017. Web. 31 Oct 2020.

Vancouver:

Dobson, Andrew 1. Compact representations for efficient robot motion planning with formal guarantees. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55459/.

Council of Science Editors:

Dobson, Andrew 1. Compact representations for efficient robot motion planning with formal guarantees. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55459/

15. Krontiris, Athanasios, 1984-. Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator.

Degree: PhD, Computer Science, 2017, Rutgers University

 Rearranging multiple objects is a critical skill for robots so that they can effectively deal with clutter in human spaces. This is a challenging problem… (more)

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APA (6th Edition):

Krontiris, Athanasios, 1. (2017). Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/55536/

Chicago Manual of Style (16th Edition):

Krontiris, Athanasios, 1984-. “Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator.” 2017. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/55536/.

MLA Handbook (7th Edition):

Krontiris, Athanasios, 1984-. “Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator.” 2017. Web. 31 Oct 2020.

Vancouver:

Krontiris, Athanasios 1. Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55536/.

Council of Science Editors:

Krontiris, Athanasios 1. Hierarchical frameworks for efficient prehensile rearrangement with a robotic manipulator. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55536/

.