⏩ Volume 21, Issue No.2, 2023 (ISR)
Development of Intelligent Robotic Systems for Autonomous Path Planning and Obstacle Avoidance in Dynamic Environments

This study focuses on the development of intelligent robotic systems capable of autonomous path planning and obstacle avoidance. By utilizing advanced algorithms, these robots can adapt to dynamic environments, ensuring effective navigation and enhanced performance in real-world scenarios such as search-and-rescue missions and industrial automation.

Christopher James Parker, Emily Grace Roberts, Daniel Joseph Clarke, William Robert Turner, Olivia Emily Harris

Paper ID: 52321201
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Design and Implementation of Robotic Grippers for Precision Handling of Complex Objects in Industrial Automation

This paper presents the design and implementation of robotic grippers tailored for precision handling of complex objects in industrial automation. These grippers utilize advanced sensors and control algorithms to manipulate delicate objects, ensuring high accuracy and efficiency in automated assembly lines and material handling applications.

John Michael Sullivan, Anna Patricia Walker, Daniel Thomas Green, Lucas Andrew Harris, Emma Rachel Moore

Paper ID: 52321202
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AI-Driven Autonomous Vehicles for Logistics and Warehousing Operations: Enhancing Efficiency and Reducing Operational Costs

This research investigates the role of AI-driven autonomous vehicles in logistics and warehousing operations. By leveraging machine learning algorithms, these vehicles optimize warehouse navigation, improve material handling, and reduce operational costs, leading to increased efficiency and reliability in logistics systems.

Samuel Patrick Wright, Olivia Claire Green, Jonathan Michael Hughes, Catherine Ann Barnes, Mark Joseph Brown

Paper ID: 52321203
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Autonomous Robot Navigation for Precision Agriculture: A Machine Learning Approach for Crop Monitoring and Yield Prediction

This paper explores the use of autonomous robots in precision agriculture, focusing on machine learning techniques for crop monitoring and yield prediction. These robots analyze data collected from sensors and cameras to provide actionable insights, aiding in decision-making and optimizing agricultural practices for higher productivity.

James Alan Walker, Sarah Emily Thompson, Daniel George Harris, Olivia Anne Smith, Christopher Michael Brown

Paper ID: 52321204
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AI-Powered Drones for Environmental Monitoring: An Intelligent System for Real-Time Data Collection and Analysis

This paper investigates the application of AI-powered drones for environmental monitoring. The drones use machine learning algorithms to collect and analyze real-time environmental data, providing insights into air quality, pollution levels, and ecosystem health, which are crucial for effective environmental management and policy-making.

William Andrew Turner, Emily Charlotte Scott, Alexander James Moore, Rebecca Grace Miller, Christopher Joseph Roberts

Paper ID: 52321205
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