ENHANCEMENT PERFORMANCE OF PATH PLANNING PROCESS BY USING OPTIMAZATION AND INTELLIGENT TRAINING ALGORITHM IN SINGLE AGENT CLOUD ROBOTIC

Hadeel Khudhur Al-Ahmedy; Saif Alabachi; Ibrahim Adel  Ibrahim

doi:10.30572/2018/KJE/170240

Authors

Hadeel Khudhur Al-Ahmedy Department of Computer Engineering, University of Technology-Iraq, Baghdad, Iraq
Saif Alabachi Department of Computer Engineering, University of Technology-Iraq, Baghdad, Iraq
Ibrahim Adel Ibrahim Department of Computer Engineering, University of Technology-Iraq, Baghdad, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170240

Keywords:

Autonomous mobile robots (AMRs), Elman neural network (ENN), Particle swarm optimization (PSO), Path planning, MQTT protocol, Single-agent robot

Abstract

The autonomous navigation of robots could be carried out by path planning that enables these robots to move efficiently and avoid obstacles even in complex areas. This work deployed metrics such as task completion time and path length to enhance the performance of path planning. Surpassing the limitations in the traditional approaches, this study improved the decision making in hard environments and ensured safe interaction between human and robots. In addition, this paper used cloud computing to increase the speed of the tasks and work easily in real time with a chance of scalability in the future. The main challenge in the path planning framework is to optimize both time and distance in single agent systems. Therefore, this challenge could be solved by presenting a hybrid path planning method that combined tow algorithms that are Particle Swarm Optimization (PSO) and Elman Neural Network (ENN). Also, this work adopted Message Queuing Telemetry Transport (MQTT) as a lightweight communication protocol to efficient exchange data between robot and cloud. The results showed that the proposed method achieved shortest completion times 5.42 seconds for case study 1, 6.82 seconds for case study 2 and a shortest path of 592.9 centimeter for case study 3. This research contributed to more efficient and reliable robotic path planning that offering a robust solution for navigating complex and dynamic environments

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