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All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan
Pohan, Muhammad Aria
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Computer Science & IT Control & Systems Engineering

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LabVIEW Libraries untuk Algoritma Perencanaan Jalur Robotik Pohan, Muhammad Aria
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 10 No. 1 (2022): TELEKONTRAN vol 10 no 1 April 2022
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v10i1.7796

Abstract

This paper presents the development of a library that can be used to create path planning algorithms for robotic systems. Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) was used to create this library of path planning algorithms. Path planning algorithms that can be created using this library are Rapidly-exploring Random Tree (RRT), RRT*, RRT*-Connect, Informed RRT*, Informed RRT*-Connect, Probabilistic Roadmap (PRM), A*, Dijkstra, Particle Swarm Optimization (PSO), and the RRT-Ant Colony System (ACS). The performance of this library is evaluated by comparing the implementation of the library in benchmark scenarios from other researchers' publications. The advantages of this library are that each subprocess can be visualized, computation time is fast, tools are available for analysis, test scenarios can be created flexibly, and algorithm parameters can be easily changed to see how it affects the algorithm's output performance. Since computation time is an essential factor in path planning algorithms, the software development of this library has been streamlined to achieve the shortest possible system compute time. One way to reduce computation time is to reduce the overhead of subVI programs. SubVI overhead is reduced by making it a subroutine. The test results show that the proposed library has a faster computation time than the comparison library. The library implementation on the robotic system was tested using the LabVIEW Robotics Simulator. We also provide examples of some developments that can be done using this library. By using this library, it is hoped that students will understand and make it easier to develop path planning algorithms for robotic systems.
Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm Pohan, Muhammad Aria
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 10 No. 2 (2022): TELEKONTRAN vol 10 no 2 Oktober 2022
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v10i2.8977

Abstract

A path planning algorithm is asymptotically optimal if it can guarantee that it will produce an optimal solution if given sufficient time. Path-planning algorithms that can provide optimal solutions are essential in many robotic applications. The objective of this study is to propose a new asymptotic optimal path planning algorithm. The method is to improve the probabilistic road map (PRM) algorithm through three strategies. The first strategy is to use an information-based sampling technique. The second strategy is that the search area starts from a small ellipsoid subset first. The third strategy is to improve the path using the wrapping process. We call this proposed algorithm the wrapping-based informed PRM (WIPRM) algorithm. Furthermore, the performance of the WIPRM algorithm was compared with the PRM algorithm, informed rapidly-exploring random tree* (RRT*), and informed PRM. The test results show that the WIPRM algorithm can build optimal paths for all test scenarios. The computational time needed by the WIPRM algorithm to build the optimal path is better than the informed RRT* and informed PRM algorithms. These results indicate that the WIPRM algorithm could be used in various robotic systems requiring optimal path planning algorithms, such as autonomous cars, unmanned aerial vehicles (UAV), and autonomous undersea vehicles (AUV).
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