Multi-hop Route Planning Based on Environment Information for Path-Following UAVs

2020 ◽  
pp. 831-839
Author(s):  
Tien-Wen Sung ◽  
Linyun Sun ◽  
Kuo-Chi Chang
Keyword(s):  
Route Planning ◽  
Path Following

An EKF Based Method for Path Following in Turbulent Air

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Caterina Grillo ◽  
F. Montano
Keyword(s):  
Path Following

Model Predictive Periodic Output Path Following

2020 ◽  
Author(s):  
Ignacio J. Sanchez ◽  
Agustina D'Jorge ◽  
Alejandro C. Limache ◽  
Alejandro H. Gonzalez ◽  
Antonio Ferramosca
Keyword(s):  
Path Following

Improved algorithm for solving multi-route planning—based on travel route planning of USA state city

2014 ◽  
Author(s):  
Liyao Yang ◽  
Yihao Guo ◽  
Qian Chen ◽  
Kailian Lin ◽  
Shiyu Lin ◽  
...  
Keyword(s):  
Route Planning ◽  
Improved Algorithm

Correction: Quasi-static experimental path-following

2019 ◽  
Author(s):  
Robin M. Neville ◽  
Rainer Groh ◽  
Alberto Pirrera ◽  
Mark Schenk
Keyword(s):  
Path Following

Path Following Control of Tractor-trailers with Off-axle Hitching

2010 ◽  
Vol 36 (9) ◽  
pp. 1272-1278 ◽  
Author(s):  
Huo-Feng ZHOU ◽  
Bao-Li MA ◽  
Li-Hui SONG ◽  
Fang-Fang ZHANG
Keyword(s):  
Path Following ◽  
Path Following Control

Tourism Route Planning Route Based on Tourist Category using Orientee ring Problem with Time Windows

2019 ◽  
Vol 1 (1) ◽  
pp. 42-49
Author(s):  
Indri Hapsari ◽  
◽  
Hazrul Is wadi ◽  
Yosvaldo Ongko Cahyadi ◽  
◽  
...  
Keyword(s):  
Time Windows ◽  
Route Planning ◽  
Ring Problem

Research on Path Following Control Strategies for Lunar Rover under Loose Soil Terrains

2014 ◽  
Vol 7 (2) ◽  
pp. 119-127
Author(s):  
Tao Zhang ◽  
Cheng Shao ◽  
Pingshu Ge ◽  
Kun W. Yang
Keyword(s):  
Control Strategies ◽  
Path Following ◽  
Lunar Rover ◽  
Path Following Control ◽  
Loose Soil

Recent advances on urban taxi sharing systems with the path arrival probability

2020 ◽  
Vol 13 ◽  
Author(s):  
Dui Hongyan ◽  
Zhang Chi
Keyword(s):  
Failure Probability ◽  
Traffic Congestion ◽  
Optimal Strategy ◽  
Time Windows ◽  
Optimal Path ◽  
Route Planning ◽  
Driving Time ◽  
Load Rate ◽  
Travel Efficiency ◽  
Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

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