TITLE:
Developing Intelligent Algorithm for Enhancing Robotic Factors Affecting Task Performance Strategical Methods
AUTHORS:
Zhenli Lu, Anas A. Nicola
KEYWORDS:
Deep Reinforcement Learning, RNN, Robot Navigation, Service Level Agreements, Task Scheduling
JOURNAL NAME:
Advances in Artificial Intelligence and Robotics Research,
Vol.2 No.1,
February
5,
2026
ABSTRACT: Effective task scheduling can significantly impact performance, productivity, and profitability in many real-world settings. Deep learning offers promising avenues in image recognition; robots can autonomously learn and optimize their actions, enhancing their performance in complex settings. Optimizing task scheduling is crucial. In particular, reinforcement learning is a promising task scheduling approach because it can learn from experience and adapt to changing conditions. Research analyzes the performance possibilities of task scheduling by using deep analysis; it allows for the selection of highly efficient environment models by using neural networks (CNNs) and recurrent neural networks (RNNs). Deep analysis allows for the selection of highly efficient environment models. Moreover, automatic selection based on optimization algorithms has been proposed. Furthermore, in this research, our finding aims to extend the applicability of deep learning in robotics, contributing to the design and implementation of future intelligent systems and improving robotic systems’ environmental perception, decision-making, and actions. To understand non-collision robot movement in unknown dynamic environments, intelligent algorithms have been proposed, showing considerable improvement in failure rate and path length. To recognize objects, navigate in complex environments, and facilitate decision-making based on real-time data, deep learning (DL) can be used to enable robots to execute and perform various complex tasks while moving freely. Furthermore, patterns have been recognized in robotic movement paths. Therefore, the authors developed a novel hybrid intelligent approach that allows and provides superior efficiency regardless of environmental parameters.