Analisis Perbandingan Algoritma Breadth First Search (BFS) dan Algoritma A*

2021 ◽  
Vol 9 (2) ◽  
pp. 139-153
Author(s):  
Valdyna Aditiya ◽  
Budi Herdiana
Keyword(s):  
Path Planning ◽  
Breadth First Search

Pada path planning terdapat beberapa metode yang dapat digunakan. Metode yang dapat digunakan untuk path planning yaitu algoritma Breadth First Search (BFS) dan algoritma A*. Metode yang digunakan tergantung dari keadaan lingkungan. Melakukan path planning pada tiap lingkungan dengan metode berbeda sehingga dapat mengetahui bagaimana peformansinya tiap metode tersebut. Sehingga tujuan penelitian ini adalah untuk membandingkan antara algoritma BFS dengan algoritma A* dalam melakukan path planning. Proses perbandingan dilakukan pada lima lingkungan pengujian. Lingkungan pengujian yang digunakan yaitu tanpa obstacle, obstacle trap, obstacle sederhana, obstacle maze dan obstacle narrow. Perbandingan algoritma BFS dan algoritma A* berdasarkan waktu eksekusi dan jumlah node yang dibutuhkan untuk melakukan path planning. Hasil penelitian menunjukkan bahwa pencarian jalur dari titik start ke titik goal dapat diselesaikan dengan algoritma BFS dan A*. Pada algoritma BFS maupun A* menghasilkan biaya jalur yang sama. Perbedaan terjadi pada node-node yang dibutuhkan algoritma BFS dan A* untuk menghasilkan jalur dari titik start hingga titik goal. Algoritma BFS membutuhkan node lebih banyak dibandingkan dengan A* untuk mencapai titik goal. Perbedaan jumlah node tersebut membuat waktu eksekusi menjadi berbeda. Waktu eksekusi pada algoritma BFS membutuhkan waktu lebih banyak dibandingkan dengan A*. Berdasarkan pengujian yang telah dilakukan maka algoritma A* lebih cepat dalam melakukan path planning. Tetapi pada lingkungan pengujian maze terjadi perbedaan waktu yang sedikit. Pada BFS memerlukan waktu tercepat 4,09 detik serta pada A* memerlukan waktu tercepat 3,88 detik. Serta pada lingkungan maze memiliki perbedaan jumlah node cukup sedikit yaitu 26 node. Hal tersebut membuktikan bahwa A* tidak selalu unggul jauh dengan BFS.

scholarly journals Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4156
Author(s):  
Luís B. P. Nascimento ◽  
Dennis Barrios-Aranibar ◽  
Vitor G. Santos ◽  
Diego S. Pereira ◽  
William C. Ribeiro ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Configuration Space ◽  
Autonomous Robot ◽  
Breadth First Search ◽  
Propagation Process ◽  
Robot Systems ◽  
And Performance ◽  
Safe Path ◽  
High Clearance

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

Path Planning for Mobile Robots in 3D Dynamic Environments

2011 ◽  
Vol 403-408 ◽  
pp. 1401-1404
Author(s):  
Li Jia Chen ◽  
He Jin ◽  
Jin Ke Bai ◽  
Hai Tao Mao
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Artificial Potential Field ◽  
Uniform Motion ◽  
Breadth First Search ◽  
Time Step ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Aiming at the robustness of the path planning of mobile robots in the 3D dynamic environment, an improved ARF (Artificial Potential Field) based path planning algorithm is proposed in this paper. Supposing that all the obstacles move regularly and the robot is on uniform motion in a grid 3D environment. Firstly, the algorithm computes the future statuses of the environment, such as the coordinate of all the obstacles and the goal, until a time step T in which there is at least one route between the start and goal. T is obtained by BFS (Breadth First Search) and environment configuration parameters. Secondly, because in every time step the environment can be consider as being static, ARF is used to determine the potential value of every space position in each time step. Finally, a route along the lowest potential values is found for the robot from the start to goal. Simulation results show that the algorithm makes the robot avoid obstacles effectively and reach the goal safely.

scholarly journals Path planning for the Platonic solids on prescribed grids by edge-rolling

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252613
Author(s):  
Ngoc Tam Lam ◽  
Ian Howard ◽  
Lei Cui
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Penrose Tiling ◽  
Platonic Solid ◽  
Breadth First Search ◽  
Platonic Solids ◽  
Science And Art ◽  
Planning Algorithm ◽  
Position And Orientation ◽  
Path Planning Algorithm

The five Platonic solids—tetrahedron, cube, octahedron, dodecahedron, and icosahedron—have found many applications in mathematics, science, and art. Path planning for the Platonic solids had been suggested, but not validated, except for solving the rolling-cube puzzles for a cubic dice. We developed a path-planning algorithm based on the breadth-first-search algorithm that generates a shortest path for each Platonic solid to reach a desired pose, including position and orientation, from an initial one on prescribed grids by edge-rolling. While it is straightforward to generate triangular and square grids, various methods exist for regular-pentagon tiling. We chose the Penrose tiling because it has five-fold symmetry. We discovered that a tetrahedron could achieve only one orientation for a particular position.

scholarly journals Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

2021 ◽  
Vol 13 (22) ◽  
pp. 4644
Author(s):  
Heba Kurdi ◽  
Shaden Almuhalhel ◽  
Hebah Elgibreen ◽  
Hajar Qahmash ◽  
Bayan Albatati ◽  
...  
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Execution Time ◽  
Autonomous Vehicles ◽  
The Moon ◽  
Breadth First Search ◽  
The Earth ◽  
Planning Algorithm ◽  
Np Hard Problem ◽  
Path Planning Algorithm

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.

Comparison between Waveform and Bug Path Planning Algorithm for Mobile Robot

2014 ◽  
Vol 607 ◽  
pp. 774-777
Author(s):  
Swee Ho Tang ◽  
Che Fai Yeong ◽  
Eileen Lee Ming Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Simulation Software ◽  
Breadth First Search ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Grid Based

Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.

Path planning strategies for laser line forming

2003 ◽  
Author(s):  
Edward Reutzel ◽  
Kevin Gombotz ◽  
Richard Martukanitz ◽  
Panagiotis Michaleris
Keyword(s):  
Path Planning ◽  
Laser Line ◽  
Planning Strategies
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