Design of Motion Control Algorithm in Robotic Arm for Obstacles Avoidance Using Vision System

Lim, Chee Kiat (2025) Design of Motion Control Algorithm in Robotic Arm for Obstacles Avoidance Using Vision System. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Industry 4.0 has brought about a new period of manufacturing effectiveness and output, marked by the incorporation of intelligent digital technologies into industrial operations. This shift, also known as the Fourth Industrial Revolution, comprises a range of technologies such as industrial Internet of Things (IoT) networks, artificial intelligence (AI), Big Data, robotics, and automation. Autonomous robots are crucial in driving the advancement of intelligent factories and smart production. The development of robotic arms in the context of Industry 4.0, with a specific emphasis on their growing autonomy and ability to avoid obstacles. With the increasing automation of production, robotic arms are taking over activities that were historically done by people. This requires improvements in their intelligence and flexibility. Incorporating obstacle avoidance strategies, enabled by sensors and algorithms, is crucial for guaranteeing safety, effectiveness, and adaptability in dynamic industrial settings. To overcome the difficulties of obstacle avoidance in robotic arms, it is necessary to foster interdisciplinary collaboration among experts in robotics, computer vision, and control theory. Robotic arms can effectively navigate intricate situations and prevent collisions with accuracy and dependability by utilizing sophisticated vision systems and machine learning algorithms. The objective of the proposed research is to create and apply cutting-edge motion control algorithms specifically designed for robotic arms that have vision systems. This will help progress robotic technology in the context of Industry 4.0. The main goals are to develop effective obstacle avoidance algorithms, incorporate them into simulated robotic armsystems, and conduct thorough performance assessments in simulated envirnoment situations. This research aims to advance robotics and automation in order to enhance safety and intelligence in manufacturing processes during the Industry 4.0 era. Rapidly-exploring Random Trees (RRT), Rapidly-exploring Random Tree - Star (RRT*), Hybrid RRT-A* and Modified Hybrid RRT-A* algorithms will be used to improve the efficiency of robotic arm in obstacle avoidance. It is expected that by modifying one of these algorithms can decrease the time consuming to generate a new path with a shortest distance and decrease the number of error occured.