Meta-ROS: A Next-Generation Middleware Architecture for Adaptive and Scalable Robotic Systems

The field of robotics faces significant challenges related to the complexity and interoperability of existing middleware frameworks, like ROS2, which can be difficult for new developers to adopt. To address these issues, we propose Meta-ROS, a novel middleware solution designed to streamline robotics development by simplifying integration, enhancing performance, and ensuring cross-platform compatibility. Meta-ROS leverages modern communication protocols, such as Zenoh and ZeroMQ, to enable efficient and low-latency communication across diverse hardware platforms, while also supporting various data types like audio, images, and video. We evaluated Meta-ROS's performance through comprehensive testing, comparing it with existing middleware frameworks like ROS1 and ROS2. The results demonstrated that Meta-ROS outperforms ROS2, achieving up to 30% higher throughput, significantly reducing message latency, and optimizing resource usage. Additionally, its robust hardware support and developer-centric design facilitate seamless integration and ease of use, positioning Meta-ROS as an ideal solution for modern, real-time robotics AI applications.

Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion

This paper presents a novel hybrid Automatic Speech Recognition (ASR) system designed specifically for resource-constrained robots. The proposed approach combines Hidden Markov Models (HMMs) with deep learning models and leverages socket programming to distribute processing tasks effectively. In this architecture, the HMM-based processing takes place within the robot, while a separate PC handles the deep learning model. This synergy between HMMs and deep learning enhances speech recognition accuracy significantly. We conducted experiments across various robotic platforms, demonstrating real-time and precise speech recognition capabilities. Notably, the system exhibits adaptability to changing acoustic conditions and compatibility with low-power hardware, making it highly effective in environments with limited computational resources. This hybrid ASR paradigm opens up promising possibilities for seamless human-robot interaction. In conclusion, our research introduces a pioneering dimension to ASR techniques tailored for robotics. By employing socket programming to distribute processing tasks across distinct devices and strategically combining HMMs with deep learning models, our hybrid ASR system showcases its potential to enable robots to comprehend and respond to spoken language adeptly, even in environments with restricted computational resources. This paradigm sets a innovative course for enhancing human-robot interaction across a wide range of real-world scenarios.