scholarly journals Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 153134-153168
Author(s):  
Xiao Zhou ◽  
Bin Sun ◽  
Sen Li ◽  
Shiyan Liu
Keyword(s):  
Data Mining ◽  
Route Planning ◽  
Planning Algorithm

scholarly journals Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 112 ◽  
Author(s):  
Xiao Zhou ◽  
Mingzhan Su ◽  
Zhong Liu ◽  
Yu Hu ◽  
Bin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Weight Function ◽  
Closed Loop ◽  
Naive Bayes ◽  
Route Planning ◽  
Naïve Bayes ◽  
Mining Machine ◽  
Planning Algorithm ◽  
Set Up

A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles

2018 ◽  
Vol 06 (04) ◽  
pp. 251-266
Author(s):  
Phillip J. Durst ◽  
Christopher T. Goodin ◽  
Cindy L. Bethel ◽  
Derek T. Anderson ◽  
Daniel W. Carruth ◽  
...  
Keyword(s):  
Path Planning ◽  
Urban Area ◽  
Route Planning ◽  
Unmanned Ground Vehicles ◽  
Membership Functions ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Sensor Error ◽  
Planning Algorithm ◽  
Error Costs

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.

scholarly journals A fast route planning algorithm for MPLS-TE

2012 ◽  
Author(s):  
Katia Sarsembagieva ◽  
Georgios Gardikis ◽  
George Xilouris ◽  
Anastasios Kourtis
Keyword(s):  
Route Planning ◽  
Fast Route ◽  
Planning Algorithm

MISSION-ADAPTABLE ROUTE PLANNING IN UNCERTAIN AND ADVERSARIAL ENVIRONMENT

2006 ◽  
Vol 15 (05) ◽  
pp. 803-821 ◽  
Author(s):  
PING YAN ◽  
MINGYUE DING ◽  
CHANGWEN ZHENG
Keyword(s):  
Real Time ◽  
A Priori ◽  
Route Planning ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Solution Quality ◽  
Model Complex ◽  
Local Methods ◽  
Domain Specific Knowledge ◽  
Planning Algorithm

In this paper, the route-planning problems of Unmanned Aerial Vehicle (UAV) in uncertain and adversarial environment are addressed, including not only single-mission route planning in known a priori environment, but also the route replanning in partially known and mission-changeable environments. A mission-adaptable hybrid route-planning algorithm based on flight roadmap is proposed, which combines existing global and local methods (Dijkstra algorithm, SAS and D*) into a two-level framework. The environment information and constraints for UAV are integrated into the procedure of building flight roadmap and searching for routes. The route-planning algorithm utilizes domain-specific knowledge and operates in real time with near-optimal solution quality, which is important to uncertain and adversarial environment. Other planners do not provide all of the functionality, namely real-time planning and replanning, near-optimal solution quality, and the ability to model complex 3D constraints.

scholarly journals Smart Tour Route Planning Algorithm Based on Clustering Center Motive Iteration Search

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 185607-185633
Author(s):  
Xiao Zhou ◽  
Mingzhan Su ◽  
Zhong Liu ◽  
De Zhang
Keyword(s):  
Route Planning ◽  
Planning Algorithm ◽  
Clustering Center
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