Scalability
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Scalability refers to a system's ability to maintain performance, efficiency, and reliability as its size, complexity, or operational demands increase. In robotics and AI, scalability manifests across multiple dimensions: algorithms must handle growing map sizes or sensor data (as in large-scale SLAM), robot collectives must coordinate effectively whether comprising tens or thousands of units (as in swarm systems like Kilobot), and learning frameworks such as deep reinforcement learning must generalize from limited trials to real-world deployment across diverse tasks. Software infrastructures like ROS 2 and simulation platforms like V-REP are explicitly designed to scale from single robots to distributed fleets. Hardware scalability appears in modular robots, additive manufacturing, and federated learning across resource-constrained IoT networks. Scalability matters because many robotic applications—search and rescue, large-scale manipulation, autonomous mapping—require solutions that remain computationally tractable and practically deployable beyond controlled laboratory conditions. Without scalable methods, promising techniques fail to transfer from benchmarks to real-world systems, making scalability a foundational concern throughout robotics research and engineering.
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