A Fast Sampling-based Optimal Route-Planning Algorithm to Satisfy Linear Temporal Logic Specifications

2018 ◽  
Author(s):  
Zetian Zhang ◽  
Raghvendra V. Cowlagi
Keyword(s):  
Temporal Logic ◽  
Route Planning ◽  
Linear Temporal Logic ◽  
Optimal Route ◽  
Planning Algorithm

scholarly journals Semantic-Aware Top-k Multirequest Optimal Route

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Shuang Wang ◽  
Yingchun Xu ◽  
Yinzhe Wang ◽  
Hezhi Liu ◽  
Qiaoqiao Zhang ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Route Planning ◽  
Location Based Services ◽  
Optimal Route ◽  
Wide Range ◽  
Potential Applications ◽  
Planning Algorithm ◽  
Keyword Searching ◽  
Path Queries ◽  
Indexing Technique

In recent years, research on location-based services has received a lot of interest, in both industry and academic aspects, due to a wide range of potential applications. Among them, one of the active topic areas is the route planning on a point-of-interest (POI) network. We study the top-k optimal routes querying on large, general graphs where the edge weights may not satisfy the triangle inequality. The query strives to find the top-k optimal routes from a given source, which must visit a number of vertices with all the services that the user needs. Existing POI query methods mainly focus on the textual similarities and ignore the semantic understanding of keywords in spatial objects and queries. To address this problem, this paper studies the semantic similarity of POI keyword searching in the route. Another problem is that most of the previous studies consider that a POI belongs to a category, and they do not consider that a POI may provide various kinds of services even in the same category. So, we propose a novel top-k optimal route planning algorithm based on semantic perception (KOR-SP). In KOR-SP, we define a dominance relationship between two partially explored routes which leads to a smaller searching space and consider the semantic similarity of keywords and the number of single POI’s services. We use an efficient label indexing technique for the shortest path queries to further improve efficiency. Finally, we perform an extensive experimental evaluation on multiple real-world graphs to demonstrate that the proposed methods deliver excellent performance.

scholarly journals The Planning Spectrum - One, Two, Three, Infinity

2007 ◽  
Vol 30 ◽  
pp. 101-132 ◽  
Author(s):  
M. Pistore ◽  
M. Y. Vardi
Keyword(s):  
Temporal Logic ◽  
Dynamic Systems ◽  
Linear Temporal Logic ◽  
Theoretic Approach ◽  
Execution Path ◽  
Existential Interpretation ◽  
Planning Algorithm ◽  
Definition Of ◽  
Planning Problems

Linear Temporal Logic (LTL) is widely used for defining conditions on the execution paths of dynamic systems. In the case of dynamic systems that allow for nondeterministic evolutions, one has to specify, along with an LTL formula f, which are the paths that are required to satisfy the formula. Two extreme cases are the universal interpretation A.f, which requires that the formula be satisfied for all execution paths, and the existential interpretation E.f, which requires that the formula be satisfied for some execution path. When LTL is applied to the definition of goals in planning problems on nondeterministic domains, these two extreme cases are too restrictive. It is often impossible to develop plans that achieve the goal in all the nondeterministic evolutions of a system, and it is too weak to require that the goal is satisfied by some execution. In this paper we explore alternative interpretations of an LTL formula that are between these extreme cases. We define a new language that permits an arbitrary combination of the A and E quantifiers, thus allowing, for instance, to require that each finite execution can be extended to an execution satisfying an LTL formula (AE.f), or that there is some finite execution whose extensions all satisfy an LTL formula (EA.f). We show that only eight of these combinations of path quantifiers are relevant, corresponding to an alternation of the quantifiers of length one (A and E), two (AE and EA), three (AEA and EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for the new language that is based on an automata-theoretic approach, and study its complexity.

Optimal Route Planning Algorithm Based on Real Traffic Network

2005 ◽  
Author(s):  
Nae-Seung Kang ◽  
Byung-Sung Kim ◽  
Ho-Joong Kim
Keyword(s):  
Route Planning ◽  
Traffic Network ◽  
Optimal Route ◽  
Planning Algorithm ◽  
Real Traffic

A novel optimal route planning algorithm for searching on the sea

2021 ◽  
pp. 1-19
Author(s):  
Y. Yang ◽  
Y. Mao ◽  
R. Xie ◽  
Y. Hu ◽  
Y. Nan
Keyword(s):  
Optimal Algorithm ◽  
Route Planning ◽  
Search And Rescue ◽  
Search Rate ◽  
Planning Problem ◽  
Optimal Route ◽  
Planning Algorithm ◽  
Discrete Programming ◽  
Maritime Search And Rescue ◽  
National Emergency

ABSTRACT Emergency search and rescue on the sea is an important part of national emergency response for marine perils. Optimal route planning for maritime search and rescue is the key capability to reduce the searching time, improve the rescue efficiency, as well as guarantee the rescue target’s safety of life and property. The main scope of the searching route planning is to optimise the searching time and voyage within the constraints of missing search rate and duplicate search rate. This paper proposes an optimal algorithm for searching routes of large amphibious aircraft corresponding to its flight characteristics and sea rescue capability. This algorithm transforms the search route planning problem into a discrete programming problem and applies the route traceback indexes to satisfy the duplicate search rate and missing search rate.

Time optimal route planning algorithm of LBS online navigation

2011 ◽  
Author(s):  
Yong Li ◽  
Shitai Bao ◽  
Kui Su ◽  
Qiushui Fang ◽  
Jingfeng Yang
Keyword(s):  
Route Planning ◽  
Optimal Route ◽  
Time Optimal ◽  
Planning Algorithm

scholarly journals The Gradational Route Planning for Aircraft Stealth Penetration Based on Genetic Algorithm and Sparse A-Star Algorithm

2018 ◽  
Vol 151 ◽  
pp. 04001 ◽  
Author(s):  
Li Maoquan ◽  
Zhang Yunfei ◽  
Li Shihao
Keyword(s):  
Genetic Algorithm ◽  
Detection Probability ◽  
Optimal Path ◽  
Route Planning ◽  
Goal Function ◽  
Optimal Route ◽  
Optimal Path Planning ◽  
Planning Algorithm ◽  
Global Optimal ◽  
The Cost

It is established for a gradational route planning algorithm which includes two layers. The first layer makes use of genetic algorithm to obtain the global optimal path by its global optimal characteristics. The second layer makes use of A* algorithm to obtain the local optimal path by its dynamic characteristic. When flying along the global optimal path, locating the new threat and confirming its performance, the aircraft can plan the local optimal path timely by A* algorithm. It is constructed for the cost function with two goals of the range and the average detection probability, which is used as the goal function for optimal path planning. Two paths that obtained from two optimal methods are merged to construct the optimal route comprehensively considering the threats and range. The simulation result shows that the cost of new optimal route is lower than the original optimal path obtained only by the genetic algorithm.It revealed that our algorithm could obtain an optimal path when a new radar threas occured.

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