Search Results - "Zongyuan Shen"

An autonomous integrated system for 3-D underwater terrain map reconstruction by Zongyuan Shen, Junnan Song, Mittal, Khushboo, Gupta, Shalabh

“…This paper presents a novel integrated approach of creating a 3-D surface map of seabed terrain using an Autonomous Underwater Vehicle (AUV) equipped with…”
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A Non-uniform Sampling Approach for Fast and Efficient Path Planning by Wilson, James P., Shen, Zongyuan, Gupta, Shalabh

“…In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform…”
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CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees by Shen, Zongyuan, Song, Junnan, Mittal, Khushboo, Gupta, Shalabh

“…This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle-rich environments. Due to sensing limitations,…”
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Aligning halloysite nanotubes in elastomer toward flexible film with enhanced dielectric constant by Chen, Jiamin, Shen, Zongyuan, Zhao, Yi, Liu, Xueqing, Chen, Feng, Liu, Jiyan

“…Naturally occurring halloysite nanotubes (HNTs) are considered electrically insulating counterparts of carbon nanotubes, and they are always randomly…”
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SMART: Self-Morphing Adaptive Replanning Tree by Shen, Zongyuan, Wilson, James P., Gupta, Shalabh, Harvey, Ryan

“…The letter presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments. SMART…”
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Multi‐objective optimization for cost‐efficient and resilient machining under tool wear by Wilson, James P., Shen, Zongyuan, Awasthi, Utsav, Bollas, George M., Gupta, Shalabh

“…With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while…”
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CPPNet: A Coverage Path Planning Network by Shen, Zongyuan, Agrawal, Palash, Wilson, James P., Harvey, Ryan, Gupta, Shalabh

“…This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network…”
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3-D Coverage Path Planning for Underwater Terrain Mapping by Shen, Zongyuan

“…This thesis presents an autonomous approach of 3-D coverage of underwater terrain using multi-level coverage trees. An autonomous underwater vehicle (AUV)…”
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T⋆-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles by Wilson, James P., Shen, Zongyuan, Gupta, Shalabh, Wettergren, Thomas A.

“…In this paper, we develop a new algorithm, called T ⋆ -Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T…”
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An Online Coverage Path Planning Algorithm for Curvature-Constrained AUVs by Shen, Zongyuan, Wilson, James P., Gupta, Shalabh

“…The paper presents an algorithm for online coverage path planning of unknown environments using curvature-constrained AUVs. Unlike point vehicles, which can…”
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Reductive transformation and detoxification mechanism of 2,4-dinitrochlorobenzene in combined zero valent iron and anaerobic-aerobic process by Shen, Jinyou, Zhou, Zongyuan, Ou, Changjin, Sun, Xiuyun, Li, Jiansheng, Han, Weiqing, Zhou, Lin, Wang, Lianjun

“…A combined zero valent iron （ZVI） and anaerobic-aerobic process was adopted for the treatment of 2,4-dinitrochlorobenzene （DNCB）- containing wastewater. The…”
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A Non-uniform Sampling Approach for Fast and Efficient Path Planning by Wilson, James P, Shen, Zongyuan, Gupta, Shalabh

“…In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform…”
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epsilon^$+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles by Shen, Zongyuan, Wilson, James P, Gupta, Shalabh

“…Global Oceans 2020, Singapore - U.S. Gulf Coast This paper presents a novel algorithm, called $\epsilon^*$+, for online coverage path planning of unknown…”
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SMART: Self-Morphing Adaptive Replanning Tree by Shen, Zongyuan, Wilson, James P, Gupta, Shalabh, Harvey, Ryan

“…IEEE Robotics and Automation Letters, 2023 The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast…”
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CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees by Shen, Zongyuan, Song, Junnan, Mittal, Khushboo, Gupta, Shalabh

“…in IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 135-142, Jan. 2022 This letter addresses the 3D coverage path planning (CPP) problem for terrain…”
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SMARRT: Self-Repairing Motion-Reactive Anytime RRT for Dynamic Environments by Shen, Zongyuan, Wilson, James, Harvey, Ryan, Gupta, Shalabh

“…This paper addresses the fast replanning problem in dynamic environments with moving obstacles. Since for randomly moving obstacles the future states are…”
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MRRT: Multiple Rapidly-Exploring Random Trees for Fast Online Replanning in Dynamic Environments by Shen, Zongyuan, Wilson, James P, Harvey, Ryan, Gupta, Shalabh

“…This paper presents a novel algorithm, called MRRT, which uses multiple rapidly-exploring random trees for fast online replanning of autonomous vehicles in…”
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epsilon^+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles by Shen, Zongyuan, Wilson, James P., Gupta, Shalabh

“…This paper presents a novel algorithm, called \epsilon^{\star}+ , for online coverage path planning of unknown environments using energy-constrained autonomous…”
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T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles by Wilson, James P, Shen, Zongyuan, Gupta, Shalabh, Wettergren, Thomas A

“…Global Oceans 2020, Singapore - U.S. Gulf Coast In this paper, we develop a new algorithm, called T$^{\star}$-Lite, that enables fast time-risk optimal motion…”
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CPPNet: A Coverage Path Planning Network by Shen, Zongyuan, Agrawal, Palash, Wilson, James P, Harvey, Ryan, Gupta, Shalabh

“…This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network…”
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