Building a Route for a Mobile Robot Based on the BRRT and A*(H-BRRT) Algorithms for the Effective Development of Technological Innovations
Building a Route for a Mobile Robot Based on the BRRT and A*(H-BRRT) Algorithms for the Effective Development of Technological Innovations |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
||
| Year of Publication : 2024 | ||
| Author : Amer Abu-Jassar, Hassan Al-Sukhni, Yasser Al-Sharo, Svitlana Maksymova, Vladyslav Yevsieiev, Vyacheslav Lyashenko |
||
| DOI : 10.14445/22315381/IJETT-V72I11P129 | ||
How to Cite?
Amer Abu-Jassar, Hassan Al-Sukhni, Yasser Al-Sharo, Svitlana Maksymova, Vladyslav Yevsieiev, Vyacheslav Lyashenko, "Building a Route for a Mobile Robot Based on the BRRT and A*(H-BRRT) Algorithms for the Effective Development of Technological Innovations," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 294-306, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P129
Abstract
The article examines the solution for the route constructing problem for a mobile robot using the BRRT (Biased Randomized Routing Table) and A*(H-BRRT) algorithms with the A*(A-star) optimizer. The use of such approaches allows to achieve the effective development of technological innovations based on mobile robots. A Python program was developed using the PhCham development environment to implement these algorithms. A study assessed the impact of changing basic parameters, such as the number of iterations and the movement step of the BRRT and A* algorithms, on the efficiency indicators of constructing a route for moving mobile robots. The study includes an analysis of execution time, length of the resulting route, route smoothness (number of turns), environmental complexity, overall route reliability and stability, and the ability to effectively deal with degenerate cases to develop technological innovation. The presented experimental results allow us to evaluate the effectiveness and applicability of the BRRT and A* algorithms for constructing optimal routes for a mobile robot in various environmental conditions. The obtained tracking results demonstrate the significant advantages of the developed H-BRRT algorithm for large maps with a size of 5000x5000 pixels compared to other algorithms developed for maps significantly smaller. The planning hour in the fragmented H-BRRT is extremely small, amounting to 0.000011 seconds, which significantly outweighs the effectiveness of other methods, where this indicator varies from 4.9 to 18.6 seconds. Wanting to expand, H-BRRT demonstrates the largest route – 24077.0 meters- determined by the map's scale and the advances to the route at great distances. Other methods, such as TG-BRRT and CW-TG-BRRT, show good results in terms of doubling down on small maps but sacrifice the calculation speed to the new H-BRRT.
Keywords
Mobile robot, Route planning, Algorithm BRRT, Algorithm A*, Optimization, Manufacturing innovation, Effective development, Industrial innovation.
References
[1] Pietro Bilancia et al., “An Overview of Industrial Robots Control and Programming Approaches,” Applied Sciences, vol. 13, no. 4, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Achim Buerkle et al., “Towards Industrial Robots as a Service (IRaaS): Flexibility, Usability, Safety and Business Models,” Robotics and Computer-Integrated Manufacturing, vol. 81, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jianjun Ni et al., “An Improved Ssd-Like Deep Network-Based Object Detection Method for Indoor Scenes,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Rodrigo Bernardo, João M.C. Sousa, and Paulo J.S. Gonçalves, “A Novel Framework to Improve Motion Planning of Robotic Systems Through Semantic Knowledge-Based Reasoning,” Computers & Industrial Engineering, vol. 182, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Dingyun Duan et al., “Industrial Robots and Firm Productivity,” Structural Change and Economic Dynamics, vol. 67, pp. 388-406, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] V. Troianskyi et al., “First Reported Observation of Asteroids 2017 AB8, 2017 QX33, and 2017 RV12,” Contributions of the Astronomical Observatory Skalnaté Pleso, vol. 53, no. 2, pp. 5-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Federico Adolfi, Jeffrey S. Bowers, and David Poeppel, “Successes and Critical Failures of Neural Networks in Capturing Human-Like Speech Recognition,” Neural Networks, vol. 162, pp. 199-211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Xiangrui Xing et al., “Robot Path Planner Based on Deep Reinforcement Learning and the Seeker Optimization Algorithm,” Mechatronics, vol. 88, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Xinning Li et al., “Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition,” Algorithms, vol. 15, no. 5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] N. Hung, and et al., “A Review of Path Following Control Strategies for Autonomous Robotic Vehicles: Theory, Simulations, and Experiments,” Journal of Field Robotics, vol. 40, no. 3, pp. 747-779, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Shiwei Lin et al., “An Intelligence-Based Hybrid PSO-SA for Mobile Robot Path Planning in Warehouse,” Journal of Computational Science, vol. 67, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Martina Benko Loknar, Gregor Klančar, and Sašo Blažič, “Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using Bézier Curves with Constraints on Velocity, Acceleration and Jerk,” Sensors, vol. 23, no. 4, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jaafar Ahmed Abdulsaheb, and Dheyaa Jasim Kadhim, “Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey,” Robotics, vol. 12, no. 4, pp. 1-35, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Wei Li et al., “CC-BRRT: A Path Planning Algorithm Based on Central Circle Sampling Bidirectional RRT,” Web Information Systems and Applications: 18th International Conference, Kaifeng, China, pp. 430-441, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Bing-Gang Jhong, and Mei-Yung Chen, “An Enhanced Navigation Algorithm with An Adaptive Controller for Wheeled Mobile Robot Based on Bidirectional RRT,” Actuators, vol. 11, no. 10, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mehmet Korkmaz, and Akif Durdu, “Comparison of Optimal Path Planning Algorithms,” 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering, Lviv-Slavske, Ukraine, pp. 255-258, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Yubo Zhang, and Daoxiong Gong, “S-BRRT*: A Spline-based Bidirectional RRT with Strategies under Nonholonomic Constraint,” 2021 33rd Chinese Control and Decision Conference, Kunming, China, pp. 1753-1758, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Xin Shu et al., “Locally Guided Multiple Bi-RRT∗ for Fast Path Planning in Narrow Passages,” 2019 IEEE International Conference on Robotics and Biomimetics, Dali, China, pp. 2085-2091, 2019. [CrossRef] [Google Scholar] [Publisher Link] [19] Pengkai Wang et al., “ABA*-Adaptive Bidirectional A* Algorithm for Aerial Robot Path Planning,” IEEE Access, vol. 11, pp. 103521-103529, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Chunlei Wang, Xiao Yang, and He Li, “Improved Q-learning Applied to Dynamic Obstacle Avoidance and Path Planning,” IEEE Access, vol. 10, pp. 92879-92888, 2022.
[CrossRef ] [Google Scholar] [Publisher Link]
[21] Karthik Karur et al., “A Survey of Path Planning Algorithms for Mobile Robots,” Vehicles, vol. 3, no. 3, pp. 448-468, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Liwei Yang et al., “Path Planning Technique for Mobile Robots: A Review,” Machines, vol. 11, no. 10, pp. 1-46, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Zeynep Gari, Durmuş Karayel, and Murat Erhan Çimen, “A Study on Path Planning Optimization of Mobile Robots Based on Hybrid Algorithm,” Concurrency and Computation: Practice and Experience, vol. 34, no. 5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shang Erke et al., “An Improved A-Star Based Path Planning Algorithm for Autonomous Land Vehicles,” International Journal of Advanced Robotic Systems, vol. 17, no. 5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Joshua Buckner et al., “pyCHARMM: Embedding CHARMM Functionality in a Python Framework,” Journal of Chemical Theory and Computation, vol. 19, no. 12, pp. 3752-3762, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Alex Martelli et al., Python in a Nutshell, O'Reilly Media, pp. 1-738, 2023.
[Google Scholar] [Publisher Link]
[27] Mustafa Tosun et al., “DAWN-Sim: A Distributed Algorithm Simulator for Wireless Ad-Hoc Networks in Python,” 2023 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, pp. 635-639, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Rainer Otterbach et al., “System Verification Throughout the Development Cycle,” ATZ Worldwide, vol. 109, pp. 5-8, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Junki Wang et al., “Hybrid Bidirectional Rapidly Exploring Random Tree Path Planning Algorithm with Reinforcement Learning,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 25, no. 1, pp. 121-129, 2021.
[CrossRef] [Google Scholar] [Publisher Link]