Optimized Graph Search Algorithms for Exploration with Mobile Robot

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.

A Cooperative Multi-Agent Based Path-Planning and Optimization Strategy for Dynamic Environment

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

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

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.

A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

<span>Unmanned Air Vehicle (UAV) has attracted attention in recent years in conducting missions for longer time with higher levels of autonomy. For the enhanced autonomous characteristic of UAV, path planning is one of the crucial issues. Current researches on the graph search algorithms under combinatorial method are mainly reviewed in this paper by keeping focus on the comprehensive surveys of its properties for path planning. The outcome is a pen picture of their assumptions and drawbacks.</span>

A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm’s success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

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

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.