scholarly journals A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 72
Author(s):  
Joana Santos ◽  
Paulo M. Rebelo ◽  
Luis F. Rocha ◽  
Pedro Costa ◽  
Germano Veiga
Keyword(s):  
Heuristic Search ◽  
State Of The Art ◽  
Routing Algorithm ◽  
Route Planning ◽  
Deadlock Avoidance ◽  
Task Execution ◽  
Simulation Experiments ◽  
Routing And Scheduling ◽  
Alternative Approach ◽  
Overall Performance

A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

Dynamic Routing Algorithm Based on HIP Multicast Model

2012 ◽  
Vol 424-425 ◽  
pp. 607-611
Author(s):  
Xue Zhen Shen ◽  
Xin Guo Tang
Keyword(s):  
Loss Rate ◽  
Multicast Routing ◽  
Search Algorithm ◽  
Routing Algorithm ◽  
Simulation Experiments ◽  
Delay Jitter ◽  
Result Show ◽  
Dynamic Multicast ◽  
Multicast Routing Algorithm ◽  
Multiple Qos

A HIP multicast mode; based dynamic multicast routing algorithm (HIPDMR) was brought out and network model was established to describe and simplify problem to be researched. HIPDMR used Bellman-Ford as routing search algorithm, which can determine dynamic multicast routing with minimum hop number and overhead while meet constraints of bandwidth, delay, jitter and packet loss rate. Simulation experiments result show that HIPDMR can build dynamic multicast routing under constraints of multiple QoS comparing with algorithms that not considering QoS assuming network node output link capacity be equal

scholarly journals Front-to-Front Heuristic Search for Satisficing Classical Planning

2020 ◽  
Author(s):  
Ryo Kuroiwa ◽  
Alex Fukunaga
Keyword(s):  
Heuristic Search ◽  
Search Strategy ◽  
State Of The Art ◽  
Heuristic Evaluation ◽  
Bidirectional Search

Although symbolic bidirectional search is successful in optimal classical planning, state-of-the-art satisficing planners do not use bidirectional search. Previous bidirectional search planners for satisficing planning behaved similarly to a trivial portfolio, which independently executes forward and backward search without the desired ``meet-in-the-middle'' behavior of bidirectional search where the forward and backward search frontiers intersect at some point relatively far from the forward and backward start states. In this paper, we propose Top-to-Top Bidirectional Search (TTBS), a new bidirectional search strategy with front-to-front heuristic evaluation. We show that TTBS strongly exhibits ``meet-in-the-middle'' behavior and can solve instances solved by neither forward nor backward search on a number of domains.

scholarly journals Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics

2021 ◽  
Vol 10 (8) ◽  
pp. 571
Author(s):  
Sumit Mishra ◽  
Nikhil Singh ◽  
Devanjan Bhattacharya
Keyword(s):  
Data Analysis ◽  
Routing Algorithm ◽  
Open Data ◽  
Route Planning ◽  
Contact Tracing ◽  
Residential Areas ◽  
Environment Exposure ◽  
Micro Mobility ◽  
Spatial System ◽  
Mobility Solution

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

scholarly journals On the Completeness of Pruning Techniques for Planning with Conditional Effects

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dunbo Cai ◽  
Sheng Xu ◽  
Tongzhou Zhao ◽  
Yanduo Zhang
Keyword(s):  
Heuristic Search ◽  
State Of The Art ◽  
The State ◽  
Search Procedure ◽  
Conditional Effects ◽  
Relevance Analysis ◽  
Pruning Strategy ◽  
Pruning Techniques

Pruning techniques and heuristics are two keys to the heuristic search-based planning. Thehelpful actionspruning (HAP) strategy andrelaxed-plan-based heuristicsare two representatives among those methods and are still popular in the state-of-the-art planners. Here, we present new analyses on the properties of HAP. Specifically, we show new reasons for which HAP can cause incompleteness of a search procedure. We prove that, in general, HAP is incomplete for planning with conditional effects if factored expansions of actions are used. To preserve completeness, we propose a pruning strategy that is based onrelevance analysisandconfrontation. We will show that bothrelevance analysisandconfrontationare necessary. We call it theconfrontation and goal relevant actionspruning (CGRAP) strategy. However, CGRAP is computationally hard to be exactly computed. Therefore, we suggest practical approximations from the literature.

Issues in the Syntactic Parsing of Queries for a Natural Language Interface to Databases

2021 ◽  
pp. 157-179
Author(s):  
Alexander Gelbukh ◽  
José A. Martínez F. ◽  
Andres Verastegui ◽  
Alberto Ochoa
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Experimental Tests ◽  
Syntactic Parsing ◽  
Natural Language Interfaces ◽  
Natural Language Interface ◽  
Overall Performance

In this chapter, an exhaustive parser is presented. The parser was developed to be used in a natural language interface to databases (NLIDB) project. This chapter includes a brief description of state-of-the-art NLIDBs, including a description of the methods used and the performance of some interfaces. Some of the general problems in natural language interfaces to databases are also explained. The exhaustive parser was developed, aiming at improving the overall performance of the interface; therefore, the interface is also briefly described. This chapter also presents the drawbacks discovered during the experimental tests of the parser, which show that it is unsuitable for improving the NLIDB performance.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

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