<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Robotics is increasingly advancing across diverse sectors of industry and daily life, with particularly promising applications in autonomous driving. This research develops a robotic autopilot task serialization management system and conducts an in-depth analysis of how robot path planning influences serialization monitoring. The path planning system (PPS) has been enhanced through the integration of a behavioral tree path planning model and hierarchical decision-making processes. The performance of the newly developed PPS algorithm is evaluated through comparative simulation experiments against established path planning algorithms, such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The findings reveal that the PPS algorithm consistently achieves superior outcomes, planning paths with the shortest average length (188.673) and the lowest standard deviation (1.1547). Furthermore, it demonstrates optimal path efficiency and minimum energy consumption compared to four other migration algorithms. Additionally, the study observes the migration rates for node radii of 60 and 120 mm, which are 26% and 70%, respectively. Notably, as the number of robots and nodes increases, the migration rate stabilizes, underscoring the scalability and effectiveness of the PPS algorithm in complex environments.</p>
</abstract>

Robotics is increasingly advancing across diverse sectors of industry and daily life, with particularly promising applications in autonomous driving. This research develops a robotic autopilot task serialization management system and conducts an in-depth analysis of how robot path planning influences serialization monitoring. The path planning system (PPS) has been enhanced through the integration of a behavioral tree path planning model and hierarchical decision-making processes. The performance of the newly developed PPS algorithm is evaluated through comparative simulation experiments against established path planning algorithms, such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The findings reveal that the PPS algorithm consistently achieves superior outcomes, planning paths with the shortest average length (188.673) and the lowest standard deviation (1.1547). Furthermore, it demonstrates optimal path efficiency and minimum energy consumption compared to four other migration algorithms. Additionally, the study observes the migration rates for node radii of 60 and 120 mm, which are 26% and 70%, respectively. Notably, as the number of robots and nodes increases, the migration rate stabilizes, underscoring the scalability and effectiveness of the PPS algorithm in complex environments.

Application of Automatic Driving Task Sequentialisation Monitoring for Band Operator Robots

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Robotics is increasingly advancing across diverse sectors of industry and daily life, with particularly promising applications in autonomous driving. This...

Application of Automatic Driving Task Sequentialisation Monitoring for Band Operator Robots

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 05 mar 2024

Przyjęty: 13 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1437

Słowa kluczowe
Behavioral tree, Hierarchical decision making, Autonomous driving, Path planning

© 2024 Yiming Bai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of Automatic Driving Task Sequentialisation Monitoring for Band Operator Robots

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 05 mar 2024

Przyjęty: 13 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1437

Słowa kluczoweBehavioral tree, Hierarchical decision making, Autonomous driving, Path planning

© 2024 Yiming Bai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Behavioral tree, Hierarchical decision making, Autonomous driving, Path planning