Integrating GeoAI and GIS for Improved Robotic Systems Design
In today's rapidly advancing technological landscape, the integration of engineering methodologies and Industry 4.0 technologies has become crucial for the design of innovative robotic systems. This article presents an innovative approach that combines Systems Engineering (SE) methodologies with Industry 4.0 (I4.0) technologies for the conceptual design of underwater exploration vehicles. Through a case study on an aquatic exploration vehicle, it illustrates how the functional approach of SE allows for the incorporation of I4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analysis to enhance the autonomy and efficiency of robotic systems.
The design of these complex systems benefits from the use of Functional Affinity Analysis (FAA) to link functionalities with technological solutions, enabling the adoption of I4.0 tools without design biases. This allows for greater adaptability of remotely operated vehicles (ROVs) in underwater missions, improving operational autonomy, real-time data capture, and user experience.
By integrating GeoAI and Geographic Information Systems ( GIS ) into the design process, engineers can leverage the power of spatial data and analysis to enhance the capabilities of robotic systems. GeoAI refers to the application of AI techniques to geospatial data, enabling advanced analysis and decision-making. GIS, on the other hand, provides a framework for capturing, storing, analyzing, and visualizing spatial data. By combining these technologies, engineers can create intelligent robotic systems that can navigate and interact with their environment more effectively.
One of the key components of this integration is the use of datasets generated by drones and orthomosaics. Drones equipped with high-resolution cameras can capture detailed images of the environment, which can then be processed to create orthomosaics. Orthomosaics are high-resolution maps that provide accurate and up-to-date spatial information. These datasets serve as valuable inputs for the design and operation of robotic systems, enabling them to navigate and perform tasks with precision.
To further enhance the capabilities of robotic systems, advanced AI techniques such as U-Net can be employed. U-Net is a deep learning architecture commonly used for image segmentation tasks. By training U-Net models on annotated datasets, engineers can teach robotic systems to recognize and classify objects in their environment. This enables them to make informed decisions and perform complex tasks autonomously.
The proposed methodology establishes a model for integrating I4.0 capabilities into the design of advanced robotic systems, providing a roadmap for the development of complex systems in the context of Industry/Society 5.0. This functional approach allows engineers to address the development of advanced systems in a structured and efficient manner.
In conclusion, the integration of GeoAI, GIS, datasets, drones, orthomosaics, and U-Net in the design and operation of robotic systems opens up new possibilities for improving their autonomy, efficiency, and user experience. By leveraging the power of spatial data, AI, and advanced analysis techniques, engineers can create intelligent systems that are capable of navigating and interacting with their environment in a more effective and efficient manner. This integration is crucial for the advancement of underwater exploration vehicles and other complex robotic systems, paving the way for the future of Industry 4.0 and beyond.