Autonomous Resource Reallocation for Performa...

Autonomous Resource Reallocation for Performance Optimization for ROS2

Author Name : Sudharsan Vaidhun Bhaskar, Ajay Shriram Kushwaha

ABSTRACT Autonomous resource reallocation for performance optimization in ROS2 (Robot Operating System 2) is a critical approach to enhancing system efficiency in robotic applications. As robotic systems become increasingly complex, managing resources such as computational power, memory, and network bandwidth in real-time is essential to ensure optimal performance. ROS2, the latest version of the widely adopted ROS framework, provides robust tools for building robotic applications, but its scalability and performance in dynamic environments remain a challenge. This paper presents a novel method for autonomous resource reallocation that dynamically adjusts resource allocation based on the system's current needs, task priorities, and environmental conditions. The approach leverages machine learning algorithms, including reinforcement learning and predictive models, to anticipate resource demands and allocate system resources in real time. It uses ROS2's middleware capabilities, such as Quality of Service (QoS) settings, to optimize data flow, task execution, and system communication. The proposed method is evaluated through simulation and real-world experiments on various robotic platforms, including autonomous vehicles and industrial robots, to demonstrate its effectiveness in reducing latency, increasing throughput, and improving task execution reliability. The results indicate that autonomous resource reallocation enhances the performance of ROS2-based systems, allowing them to handle unpredictable workloads and operate efficiently in complex environments. This research contributes to the ongoing development of adaptive and resilient robotic systems by providing a scalable solution for autonomous resource management in ROS2 environments.