scholarly journals Optimized Graph Search Algorithms for Exploration with Mobile Robot

2021 ◽  
Vol 9 (2) ◽  
pp. 222-238
Author(s):  
Aydın GULLU ◽  
Hilmi KUŞÇU
Keyword(s):  
Mobile Robot ◽  
Shortest Path ◽  
Hybrid Algorithm ◽  
Experimental Studies ◽  
Search Algorithms ◽  
Graph Search ◽  
Dijkstra Algorithm ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Graph Search Algorithms

Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS)  and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.

scholarly journals Facilitating Large-Scale Graph Search Algorithms with Lock-Free Concurrent Pairing Heaps

2019 ◽  
Author(s):  
Jeremy Mayeres ◽  
Charles Newton ◽  
Helena Arpudaraj
Keyword(s):  
Data Structures ◽  
Shortest Path ◽  
Large Scale ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Graph Search ◽  
Dijkstra’S Algorithm ◽  
Single Source ◽  
Dijkstra's Algorithm ◽  
Graph Search Algorithms

This paper introduces a lock-free version of a Pairing heap. Dijkstra's algorithm is a search algorithm to solve the single-source shortest path problem. The performance of Dijkstra's algorithm improves when threads can also perform work concurrently (in particular, when decreaseKey calls occur concurrently.) However, current implementations of decreaseKey on popular backing data structures such as Pairing heaps and Fibonacci heaps severely limit concurrency. Lock-free techniques can improve the concurrency of search structures such as heaps. In this paper we introduce decreaseKey and insert operators for Pairing heaps that provide lock-free guarantees while still running in constant time.

Formalization of graph search algorithms and its applications

1998 ◽  
pp. 479-496 ◽  
Author(s):  
Mitsuharu Yamamoto ◽  
Koichi Takahashi ◽  
Masami Hagiya ◽  
Shin-ya Nishizaki ◽  
Tetsuo Tamai
Keyword(s):  
Search Algorithms ◽  
Graph Search ◽  
Graph Search Algorithms

scholarly journals D* lite algorithm based path planning of mobile robot in static Environment

2012 ◽  
pp. 286-290
Author(s):  
Pradipta kumar Das ◽  
S .N. Patro ◽  
C. N. Panda ◽  
Bunil Balabantaray
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Shortest Path ◽  
Target Position ◽  
Initial Position ◽  
Distance Metric ◽  
Unknown Environment ◽  
Feasible Path ◽  
The Cost

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.

scholarly journals Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis (Preprint)

2017 ◽  
Author(s):  
Isabel Segura Bedmar ◽  
Paloma Martínez ◽  
Adrián Carruana Martín
Keyword(s):  
Search Algorithms ◽  
Biomedical Literature ◽  
Graph Search ◽  
Semantic Indexing ◽  
Graph Database ◽  
Medical Subject Headings ◽  
Support Tool ◽  
Mesh Terms ◽  
Lowest Common Ancestor ◽  
Graph Search Algorithms

BACKGROUND Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. RESULTS Our experiments show promising results with an F1 of 69% on the test dataset. CONCLUSIONS To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

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