A Swarm Robotics Approach to Decontamination

2013 ◽  
pp. 107-122
Author(s):  
Daniel S. F. Alves ◽  
E. Elael M. Soares ◽  
Guilherme C. Strachan ◽  
Guilherme P. S. Carvalho ◽  
Marco F. S. Xaud ◽  
...  
Keyword(s):  
Swarm Robotics ◽  
Target Space ◽  
Graph Search ◽  
Search Problem ◽  
Ground Floor ◽  
Chemical Decontamination ◽  
Minimum Number

Many interesting and difficult practical problems need to be tackled in the areas of firefighting, biological and/or chemical decontamination, tactical and/or rescue searches, and Web spamming, among others. These problems, however, can be mapped onto the graph decontamination problem, also called the graph search problem. Once the target space is mapped onto a graph G(N,E), where N is the set of G nodes and E the set of G edges, one initially considers all nodes in N to be contaminated. When a guard, i.e., a decontaminating agent, is placed in a node i ??N, i becomes (clean and) guarded. In case such a guard leaves node i, it can only be guaranteed that i will remain clean if all its neighboring nodes are either clean or clean and guarded. The graph decontamination/search problem consists of determining a sequence of guard movements, requiring the minimum number of guards needed for the decontamination of G. This chapter presents a novel swarm robotics approach to firefighting, a conflagration in a hypothetical apartment ground floor. The mechanism has been successfully simulated on the Webots platform, depicting a firefighting swarm of e-puck robots.

scholarly journals A Swarm Robotics Approach to Decontamination

Robotics ◽  
2013 ◽  
pp. 955-969
Author(s):  
Daniel S. F. Alves ◽  
E. Elael M. Soares ◽  
Guilherme C. Strachan ◽  
Guilherme P. S. Carvalho ◽  
Marco F. S. Xaud ◽  
...  
Keyword(s):  
Swarm Robotics ◽  
Target Space ◽  
Graph Search ◽  
Search Problem ◽  
Ground Floor ◽  
Chemical Decontamination ◽  
Minimum Number

Many interesting and difficult practical problems need to be tackled in the areas of firefighting, biological and/or chemical decontamination, tactical and/or rescue searches, and Web spamming, among others. These problems, however, can be mapped onto the graph decontamination problem, also called the graph search problem. Once the target space is mapped onto a graph G(N,E), where N is the set of G nodes and E the set of G edges, one initially considers all nodes in N to be contaminated. When a guard, i.e., a decontaminating agent, is placed in a node i ? N, i becomes (clean and) guarded. In case such a guard leaves node i, it can only be guaranteed that i will remain clean if all its neighboring nodes are either clean or clean and guarded. The graph decontamination/search problem consists of determining a sequence of guard movements, requiring the minimum number of guards needed for the decontamination of G. This chapter presents a novel swarm robotics approach to firefighting, a conflagration in a hypothetical apartment ground floor. The mechanism has been successfully simulated on the Webots platform, depicting a firefighting swarm of e-puck robots.

scholarly journals Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition

2020 ◽  
Vol 6 (11) ◽  
pp. 112
Author(s):  
Faisal R. Al-Osaimi
Keyword(s):  
Search Algorithm ◽  
Recognition Performance ◽  
Feature Space ◽  
Training Data ◽  
Equivalence Relations ◽  
Graph Search ◽  
Search Problem ◽  
New Approach ◽  
Unique Approach ◽  
Training Examples

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

scholarly journals Gastroscopic Image Graph: Application to Noninvasive Multitarget Tracking under Gastroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Wang ◽  
Weiling Hu ◽  
Jiquan Liu ◽  
Jianmin Si ◽  
Huilong Duan
Keyword(s):  
Disease Diagnosis ◽  
Optical Biopsy ◽  
Graph Search ◽  
Gastric Disease ◽  
Multitarget Tracking ◽  
Search Problem ◽  
Stomach Body ◽  
Attractive Candidate ◽  
The Mean

Gastroscopic examination is one of the most common methods for gastric disease diagnosis. In this paper, a multitarget tracking approach is proposed to assist endoscopists in identifying lesions under gastroscopy. This approach analyzes numerous preobserved gastroscopic images and constructs a gastroscopic image graph. In this way, the deformation registration between gastroscopic images is regarded as a graph search problem. During the procedure, the endoscopist marks suspicious lesions on the screen and the graph is utilized to locate and display the lesions in the appropriate frames based on the calculated registration model. Compared to traditional gastroscopic lesion surveillance methods (e.g., tattooing or probe-based optical biopsy), this approach is noninvasive and does not require additional instruments. In order to assess and quantify the performance, this approach was applied to stomach phantom data andin vivodata. The clinical experimental results demonstrated that the accuracy at angularis, antral, and stomach body was 6.3 ± 2.4 mm, 7.6 ± 3.1 mm, and 7.9 ± 1.6 mm, respectively. The mean accuracy was 7.31 mm, average targeting time was 56 ms, and thePvalue was 0.032, which makes it an attractive candidate for clinical practice. Furthermore, this approach provides a significant reference for endoscopic target tracking of other soft tissue organs.

scholarly journals Efficient Breadth-First Reduct Search

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 833
Author(s):  
Veera Boonjing ◽  
Pisit Chanvarasuth
Keyword(s):  
Information System ◽  
Search Strategy ◽  
Search Space ◽  
Graph Search ◽  
Search Problem ◽  
Breadth First Search ◽  
Time And Space ◽  
First Order ◽  
Pruning Rule

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

Graph Based Path Planning

2012 ◽  
pp. 26-53
Keyword(s):  
Motion Planning ◽  
Graph Search ◽  
Robot Motion ◽  
Search Problem ◽  
A Algorithm ◽  
Daily Lives ◽  
Advantages And Disadvantages ◽  
Graph Search Algorithms ◽  
Key Steps ◽  
Standard Graph

Graphs are keenly studied by people of numerous domains as most of the applications we encounter in our daily lives can be easily given a graph-based representation. All the problems may then be easily studied as grap-based problems. In this chapter, the authors study the problem of robot motion planning as a graph search problem. The key steps involve the representation of the problem as a graph and solving the problem as a standard graph search problem. A number of graph search algorithms exist, each having its own advantages and disadvantages. In this chapter, the authors explain the concept, working methodology, and issues associated with some of these algorithms. The key algorithms under discussion include Breadth First Search, Depth First Search, A* Algorithm, Multi Neuron Heuristic Search, Dijkstra’s Algorithm, D* Algorithm, etc. Experimental results of some of these algorithms are also discussed. The chapter further presents the advantages and disadvantages of graph-based motion planning.

scholarly journals Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

2021 ◽  
Vol 11 (5) ◽  
pp. 2383
Author(s):  
Zool Hilmi Ismail ◽  
Mohd Ghazali Mohd Hamami
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Swarm Robotics ◽  
Empty Space ◽  
Difficult Problem ◽  
Search Problem ◽  
Target Search ◽  
Swarm Robots ◽  
Collaboration And Communication ◽  
Target Searching

Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism.

On the performance of Hopfield network for graph search problem

1997 ◽  
Vol 14 (4) ◽  
pp. 365-381 ◽  
Author(s):  
Gursel Serpen ◽  
Azadeh Parvin
Keyword(s):  
Hopfield Network ◽  
Graph Search ◽  
Search Problem
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