AI-Powered Intelligent Robotic System for Autonomous Navigation, Obstacle Avoidance, Visual Tracking and Object Manipulation

DOI

10.2991/978-94-6463-858-5_173How to use a DOI?

Keywords

Artificial Intelligence; Robotics; Deep Learning; Reinforcement Learning; Object Manipulation; Autonomous Navigation

Abstract

Autonomous robotic systems have gained significant attention due to their potential applications in industrial automation, surveillance, logistics, and assistive technologies. This paper presents an Intelligent Robotic System that integrates multiple AI-driven techniques, including You Only Look Once (YOLO), Faster R-CNN, Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Deep SORT, and Generative Adversarial Networks (GANs) to perform four critical tasks: line following, obstacle avoidance, visual tracking, and object manipulation. Visual tracking is used in the proposed system which has YOLO (You Only Look Once) and Faster R-CNN to do object detection with visual detection for high accuracy in real time. We combine Deep Q-Networks (DQN), Reinforcement learning; Proximal Policy Optimization (PPO) improve path finding, obstacle avoidance and adapt to some degree of dynamic environment clothes in the robot’s way GAN (Generative Adversarial Networks) is incorporated to augment perception, with synthetic training data generated by GAN; s object detection and recognition capabilities improve. Results show that the AI-based robotic system, in terms of response time, adaptability, accuracy and energy efficiency as well computational efficiency outperforms all available methods. Combining deep learning and reinforcement learning helps to make decisions in real-time by the robot, navigating through arranged environments as well interacting with changing surroundings. Different intelligent techniques comparative study on that aforesaid system shows that the proposed system is mostly robust, adaptable and power-efficient, and could be employed for autonomous mobility applications like Industrial Automation using robotics or Warehouse robotics or assistive technologies. This work contributes to the robotics field with utilization of state-of-the-art AI methodologies integrated cleanly with real-time perception, decision making and action execution clear steps towards the next generation of intelligent robots. In applications of radius limitation increased communication overhead.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - C. Sathish Kumar
AU  - Aakarsh Aryan
AU  - P. Bala
AU  - B. Kathiravan
AU  - M. Manobala
PY  - 2025
DA  - 2025/11/04
TI  - AI-Powered Intelligent Robotic System for Autonomous Navigation, Obstacle Avoidance, Visual Tracking and Object Manipulation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2070
EP  - 2080
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_173
DO  - 10.2991/978-94-6463-858-5_173
ID  - Kumar2025
ER  -