Year: 2025
Type: article
Abstract: : AdaptX is an AI-powered autonomous ground robot designed for autonomous navigation, high-resolution 3D environmental mapping, and real-time decision-making. AdaptX, which was created with scalability and adaptability in mind, combines robotic technologies and cutting-edge artificial intelligence to address important issues in autonomous mobility. The system makes use of YOLOv9 for fast object recognition, Simultaneous Localization and Mapping (SLAM) for precise real-time mapping and localization, and a web-based control interface for smooth remote monitoring and communication. AdaptX uses a combination of LiDAR and camera sensors, powered by the NVIDIA Jetson Nano, to provide dependable autonomous navigation and strong environmental awareness. AdaptX's plug-and-play hardware architecture is a noteworthy feature that enables quick integration and replacement of sensor modules or computing components without requiring significant software changes. This modular design makes platform adaption, maintenance, and upgrades easier while guaranteeing versatility for a wide range of applications. The system's potential for quick deployment in dynamic contexts and across many use cases is further increased by its plug-and-play functionality. This study provides a thorough analysis of the software architecture, system design, and implementation approach of AdaptX. The system's ability to autonomously navigate complex and dynamic landscapes, recognize and react to impediments with high precision, and create real-time 3D maps for spatial awareness is demonstrated via performance evaluation in a variety of terrain situations. Results from experiments confirm the robot's dependability and efficiency in real-world situations. AdaptX has great potential in a number of fields, such as automated surveillance, smart agriculture, industrial automation, and disaster relief. To further cement AdaptX's position as a flexible solution for next-generation autonomous robotic systems, future developments will concentrate on incorporating deep learning models for enhanced contextual comprehension, behavior prediction, and adaptive decision-making.
Author: Sayali Belhe
Institution: AISSMS Institute of Information Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411112167