Spatiotemporal scenario data-driven decision for the path planning of multiple UASs-Reference-Cited by-同舟云学术

Spatiotemporal scenario data-driven decision for the path planning of multiple UASs

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering - SCOPE '19
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Author:

He Chenyuan¹,Wan Yan¹,Xie Junfei²

Affiliation:

1. University of Texas at Arlington

2. Texas A&M University at Corpus Christi

Funder

NSF

Publisher

ACM Press

Reference18 articles.

1. Chalee Asavathiratham. 2001.The influence model: A tractable representation for the dynamics of networked markov chains.Ph.D. Dissertation. Massachusetts Institute of Technology.

2. Kevin Bollino and L Ryan Lewis. 2008. Collision-free multi-UAV optimal path planning and cooperative control for tactical applications. InAIAA Guidance, Navigation and Control Conference and Exhibit.Honolulu, Hawaii.

3. David Fridovich-Keil, Sylvia L Herbert, Jaime F Fisac, Sampada Deglurkar, and Claire J Tomlin. 2018. Planning, fast and slow: A framework for adaptive real-time safe trajectory planning. In2018 IEEE International Conference on Robotics and Automation (ICRA).Brisbane, Australia.

4. Jiawei Han, Micheline Kamber, and Anthony KH Tung. 2001. Spatial clustering methods in data mining.Geographic data mining and knowledge discovery(2001), 188--217.

5. Chenyuan He and Yan Wan. submitted, 2019. Clustering Stochastic Weather Scenarios using Influence Model-based Distance Measures. InAIAA Aviation Conference.Dallas, TX.

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1. Integral reinforcement learning‐based approximate minimum time‐energy path planning in an unknown environment;International Journal of Robust and Nonlinear Control;2020-10-08

2. Clustering Stochastic Weather Scenarios Using Influence Model-based Distance Measures;AIAA Aviation 2019 Forum;2019-06-14