Building a Route for a Mobile Robot Based on the BRRT and A*(H-BRRT) Algorithms for the Effective Development of Technological Innovations - Amer Abu-Jassar; Hassan Al-Sukhni; Yasser Al-Sharo; Maksymova, S.; Yevsieiev, V.; Lyashenko, V. (SSRG, 2024)
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; Biased Randomized Routing Table