Aplikasi Pencarian Jalur Terpendek Pada Rumah Sakit Umum Bahteramas Menggunakan Algoritma a* (A-star)

Pencarian jalur terpendek merupakan suatu permasalahan yang sering terjadi pada pengunjung rumah sakit untuk menemukan gedung atau ruangan yang dicari. Salah satu contohnya adalah pada Rumah Sakit Umum Bahteramas. Karena banyaknya gedung dan ruangan yang ada pada rumah sakit tersebut, mengakibatkan pengunjung kesulitan menemukan gedung dan ruangan yang dicari. Oleh karena itu dibutuhkan sistem yang dapat menunjukkan lokasi gedung dan ruangan beserta jalur terpendeknya, agar waktu pencarian lebih efisien. Terdapat beberapa algoritma pencarian jalur terpendek, salah satunya adalah algoritma A* (A-Star). Algoritma A* menggunakan estimasi jarak terdekat untuk mencapai tujuan (goal) dan memiliki nilai heuristik yang digunakan sebagai dasar pertimbangan. Heuristik adalah kriteria, metoda, atau prinsip-prinsip untuk menentukan pilihan sejumlah alternatif untuk mencapai sasaran dengan efektif. Hasil pada penelitian ini adalah aplikasi yang dapat menentukan jalur terpendek antara gedung dan antara ruangan yang diimplementasikan pada Operating System Android dan dibangun dengan menggunakan bahasa pemrograman Actionscript 3

Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT). BIT* uses a heuristic to efficiently search a series of increasingly dense implicit RGGs while reusing previous information. It can be viewed as an extension of incremental graph-search techniques, such as Lifelong Planning A* (LPA*), to continuous problem domains as well as a generalization of existing sampling-based optimal planners. It is shown that it is probabilistically complete and asymptotically optimal. We demonstrate the utility of BIT* on simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^8$ and manipulation problems on CMU's HERB, a 14-DOF two-armed robot. On these problems, BIT* finds better solutions faster than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at http://www.youtube.com/watch?v=TQIoCC48gp

TopCom: Index for Shortest Distance Query in Directed Graph

Finding shortest distance between two vertices in a graph is an important problem due to its numerous applications in diverse domains, including geo-spatial databases, social network analysis, and information retrieval. Classical algorithms (such as, Dijkstra) solve this problem in polynomial time, but these algorithms cannot provide real-time response for a large number of bursty queries on a large graph. So, indexing based solutions that pre-process the graph for efficiently answering (exactly or approximately) a large number of distance queries in real-time is becoming increasingly popular. Existing solutions have varying performance in terms of index size, index building time, query time, and accuracy. In this work, we propose T OP C OM , a novel indexing-based solution for exactly answering distance queries. Our experiments with two of the existing state-of-the-art methods (IS-Label and TreeMap) show the superiority of T OP C OM over these two methods considering scalability and query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic graph) structure in the graph, which makes it significantly faster than the existing methods if the SCCs (strongly connected component) of the input graph are relatively small

BHFFA*: Un nuevo algoritmo admisible de búsqueda bidireccional

A pesar de que inicialmente hubo un gran interés en los algoritmos de búsqueda bidireccionales, muy pronto se pensó que garantizar la optimalidad de las soluciones encontradas de este modo era muy complicado, y por ello se desestimó esta línea de investigación. En este artículo se muestra, sin embargo, que es posible superar los principales inconvenientes de la búsqueda bidireccional y desarrollar un nuevo algoritmo admisible, con una heurística consistente, y en términos muy sencillos. Además, a diferencia de otras implementaciones bidireccionales, la que se muestra aquí puede resultar en reducciones del tiempo necesario y de la memoria consumida de hasta el 99%, y siempre superior a su implementación unidireccional. Para constatarlo, se han estudiado dos dominios radicalmente diferentes: el grafo del Metro de Madrid y el juego del N-‘Puzle’

APLIKASI PENCARIAN JALUR TERPENDEK PADA RUMAH SAKIT UMUM BAHTERAMAS MENGGUNAKAN ALGORITMA A* (A-STAR)

Pencarian jalur terpendek merupakan suatu permasalahan yang sering terjadi pada pengunjung rumah sakit untuk menemukan gedung atau ruangan yang dicari. Salah satu contohnya adalah pada Rumah Sakit Umum Bahteramas. Karena banyaknya gedung dan ruangan yang ada pada rumah sakit tersebut, mengakibatkan pengunjung kesulitan menemukan gedung dan ruangan yang dicari. Oleh karena itu dibutuhkan sistem yang dapat menunjukkan lokasi gedung dan ruangan beserta jalur terpendeknya, agar waktu pencarian lebih efisien. Terdapat beberapa algoritma pencarian jalur terpendek, salah satunya adalah algoritma A* (A-Star). Algoritma A* menggunakan estimasi jarak terdekat untuk mencapai tujuan (goal) dan memiliki nilai heuristik yang digunakan sebagai dasar pertimbangan. Heuristik adalah kriteria, metoda, atau prinsip-prinsip untuk menentukan  pilihan sejumlah alternatif untuk mencapai sasaran dengan efektif. Hasil pada penelitian ini adalah aplikasi yang dapat menentukan jalur terpendek antara gedung dan antara ruangan yang diimplementasikan pada Operating System Android dan dibangun dengan menggunakan bahasa pemrograman Actionscript 3.Kata kunci : algoritma A* (A-Star), android, actionscript 3, jalur terpendek

Bidirectional Heuristic Search Reconsidered

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.Comment: See http://www.jair.org/ for any accompanying file

Discrete Planning

This chapter provides introductory concepts that serve as an entry point into other parts of the book. The planning problems considered here are the simplest to describe because the state space will be finite in most cases. When it is not finite, it will at least be countably infinite (i.e., a unique integer may be assigned to every state). Therefore, no geometric models or differential equations will be needed to characterize the discrete planning problems. Furthermore, no forms of uncertainty will be considered, which avoids complications such as probability theory. All models are completely known and predictable. There are three main parts to this chapter. Sections 2.1 and 2.2 define and present search methods for feasible planning, in which the only concern is to reach a goal state. The search methods will be used throughout the book in numerous other contexts, including motion planning in continuous state spaces. Following feasible planning, Section 2.3 addresses the problem of optimal planning. The principle of optimality, or the dynamic programming principle, [1] provides a key insight that greatly reduces the computation effort in many planning algorithms

Discrete particle swarm optimization for combinatorial problems with innovative applications.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

Reliable dynamic in-vehicle navigation

Having started off from luxury makes and models, in-vehicle navigation systems are now gradually spreading through the entire vehicle fleet, as drivers appreciate their usefulness. Increasingly sophisticated systems are being developed, having much more advanced functions than simple driving directions. This thesis presents a new approach for in-vehicle navigation, in which travel time reliability is incorporated in the route finding component of the navigation system. Based on historical traffic data and in the absence of current traffic information, positions in the road network at which it is likely to encounter delays, are predicted and avoided as much as possible by the route finding algorithm. The thesis starts by reviewing shortest path algorithms and conjectures that the most appropriate algorithm to use is A*, which forms a vital part of the approach developed. Performing multiple runs of A* forwards and backwards on the road network, efficiency of the route finding procedure is achieved. The time-dependent version of the algorithm is also derived. Then, the thesis goes on to define reliability on a single link of the road network as the maximum delay that can be encountered with 90% confidence and extends this definition to derive the reliability of entire routes. Having introduced the route finding procedure and the concept of reliability, the thesis presents the in-vehicle navigation approach, which involves computing a more reliable route from the driver's origin to his/her destination than the fastest, if this is unreliable. Additionally, the approach aims at computing multiple alternative partially disjoint but equivalently reliable routes to the driver, such that the congestion feedback effect can be avoided as much as possible, without the need of carrying out a dynamic traffic assignment, which would be impracticable in an in-vehicle system. A number of constraints are introduced so as to ensure that the resulting routes are acceptable to the driver (are not too long, etc). Hence, the main concept lies in initially computing the fastest time-dependent route, then applying penalties to the links characterised as unreliable (increasing the link weights in inverse proportion to their reliability) and re-running the route finding algorithm so as to find a more reliable route. After each run, the route obtained is checked against the constraints and if it does not satisfy them, it is discarded, the penalties are reduced and a new route is sought. In order to obtain alternative partially disjoint routes, penalties are also applied to links that are already included in a previously computed and accepted route. The new algorithm, RDIN, is thus presented and mathematically formulated. An extension to RDIN for re-routing, RDIN-R, is also developed. The software tool developed for the application of RDIN and RDIN-R, the Adaptive Reliable Imperial Advanced Navigation Engine (ARIAdNE) is described. A simulation example is given for demonstration and preliminary validation; then a number of field experiments are carried out in Central London to test the method in a real road network environment and to compare its performance with an existing conventional car navigation system. The results suggest that the method is workable and precise, while at the same time it is a promising step forward in the field of in-vehicle navigation