Journal of Advanced Research in Intelligence Systems and Robotics

Optimization of Pick and Place Robot Task Scheduling Operations in a Warehouse using Metaheuristics

P. Sivasankaran — 2026-04-27

Warehouse automation plays a critical role in modern logistics and supply chain management. Autonomous pick-and-place robots improve operational efficiency by automating storage and retrieval operations. However, inefficient task scheduling may lead to increased travel distance, robot congestion, and reduced system throughput. This research proposes a metaheuristic-based optimisation framework for scheduling warehouse pick-and-place robot tasks. The proposed approach utilises Genetic Algorithm (GA) and Tabu Search (TS) to minimise robot travel distance and task completion time. A warehouse simulation model is developed to evaluate algorithm performance. Experimental results demonstrate that the hybrid GA–Tabu method significantly improves scheduling efficiency compared with conventional approaches.

Advancements in Intelligent Autonomous Robotic Systems: A Review of Artificial Intelligence Integration and Real-World Applications

Riya Patel — 2026-05-10

The integration of artificial intelligence (AI) into autonomous robotic systems has significantly transformed modern engineering and industrial practices. Recent advancements in machine learning, computer vision, and sensor fusion have enabled robots to perform complex tasks
with minimal human intervention. The objective of this review is to analyze the evolution, current trends, and challenges associated with intelligent autonomous robotic systems. A systematic literature review was conducted using major scientific databases, focusing
on publications from the past decade. Studies related to AI-driven robotics, including reinforcement learning, deep learning, and human robot interaction, were included. The findings indicate that intelligent robots are increasingly being deployed in sectors such as healthcare, manufacturing, agriculture, and transportation. However, issues related
to safety, scalability, ethical concerns, and real-time decision-making remain significant challenges. The review concludes that while AI powered robotics holds immense potential, further interdisciplinary research is required to address existing limitations and ensure reliable, secure, and ethical deployment in real-world environments.

How to cite this article:
Patel R, Gupta N, Mehta A, Advancements in
Intelligent Autonomous Robotic Systems: A
Review of Artificial Intelligence Integration and
Real-World Applications. J Adv Res Intel Sys Robot
2026; 8(1). 12-15.

Deep Reinforcement Learning-Based Intelligent Control Systems For Industrial Automation

Deepika Srivastava — 2026-05-11

Background: Industrial automation has evolved significantly with the
integration of intelligent technologies, enabling enhanced productivity,
precision, and operational efficiency. However, traditional control
strategies such as Proportional-Integral-Derivative (PID) controllers
and rule-based systems are limited in their ability to handle complex,
nonlinear, and dynamic industrial environments. Deep reinforcement
learning (DRL), a combination of reinforcement learning and deep
neural networks, has emerged as a powerful approach for developing
adaptive and autonomous control systems.

Objective: This study aims to design, implement, and evaluate a DRL-
based intelligent control system for industrial automation, focusing on improving system adaptability, efficiency, and decision-makingcapabilities in complex environments.
Methods: A simulation-based experimental framework was developed
using DRL algorithms, including Deep Q-Network (DQN) and Proximal
Policy Optimization (PPO). The system was implemented using Python,
TensorFlow, and OpenAI Gym. Industrial scenarios such as robotic arm
control and process optimization were simulated. Performance metrics
including convergence rate, energy consumption, response time, and
control accuracy were analyzed.
Key Findings: The results demonstrate that DRL-based control systems
outperform traditional approaches, achieving up to 30% improvement
in efficiency, reduced energy consumption, and faster adaptation
to environmental changes. PPO exhibited superior stability and
convergence compared to DQN.
Conclusion: DRL-based intelligent control systems offer a scalable and
robust solution for modern industrial automation, enabling real-time
decision-making and adaptive control in complex environments.

Digital and Robotic Transformation of Surgery: From Image Guidance To Autonomous Intervention

Isha Kapoor — 2026-05-18

Robotic and digital technologies are revolutionizing modern surgical
practices by enabling a transition from conventional image-guided
procedures to advanced semi-autonomous and autonomous
interventions. The integration of high-resolution imaging modalities such
as magnetic resonance imaging (MRI), computed tomography (CT), and
real-time intraoperative imaging has significantly enhanced the accuracy
of surgical planning and execution. In parallel, advancements in sensing
technologies and intelligent control systems have enabled precise
instrument tracking, real-time feedback, and adaptive decision-making.
Artificial intelligence (AI) and machine learning (ML) algorithms further
enhance robotic capabilities by enabling predictive analytics, motion
optimization, and autonomous assistance during surgical procedures.
Emerging technologies such as digital twins, edge computing, and
cloud-based platforms are facilitating real-time data processing,
simulation, and remote surgical interventions. These innovations
have improved surgical outcomes by reducing human error, minimizing
invasiveness, and accelerating patient recovery. However, challenges
such as high costs, system complexity, ethical considerations, and
regulatory requirements remain significant barriers. This review provides
a comprehensive analysis of the digital and robotic transformation of
surgery, highlighting key technologies, applications, challenges, and
future directions.