ElasticROS：面向雾与云机器人的弹性协作机器人操作系统
ElasticROS: An Elastically Collaborative Robot Operation System for Fog and Cloud Robotics
AI总结：
针对机器人因集成大模型导致算力与电池不足的问题，提出基于ROS/ROS2的算法级弹性协作机器人操作系统ElasticROS，通过在线学习算法ElasAction动态分配计算任务，经实验验证性能优于现有节点级系统。
摘要：
机器人正集成更多大尺寸模型以丰富功能、提升精度，这导致计算压力失控，进而使机器人在算力和电池容量方面遭遇瓶颈。雾机器人或云机器人是解决这些问题最受期待的理论方向之一。云机器人技术方案已从系统级发展到节点级，但当前的节点级系统灵活性不足，无法动态适应变化的环境条件。\n为解决这一问题，我们提出ElasticROS，将现有的节点级系统演进为算法级系统。ElasticROS基于ROS和ROS2构建，是雾与云机器人领域首个具备算法级协作计算能力的机器人操作系统。ElasticROS开发了弹性协作计算机制，以实现对动态条件的适应性。协作计算算法是ElasticROS的核心与难点。我们对问题进行了抽象，随后提出名为ElasAction的算法来解决该问题。这是一种基于在线学习的动态动作决策算法，用于决定机器人与服务器的协作方式。该算法会动态更新参数，以适应机器人当前所处环境的条件变化，能够根据配置将计算任务弹性分配给机器人和服务器。此外，我们证明ElasAction的遗憾上界是次线性的，这保证了算法的收敛性，从而使ElasticROS的弹性具备稳定性。\n最后，我们使用ElasticROS在SLAM、抓取和人机对话等常见机器人任务上开展了实验，测量了其在延迟、CPU使用率和功耗方面的性能。这款算法级的ElasticROS性能显著优于当前的节点级系统。
英文摘要：
Robots are integrating more huge-size models to enrich functions and improve accuracy, which leads to out-of-control computing pressure. And thus robots are encountering bottlenecks in computing power and battery capacity. Fog or cloud robotics is one of the most anticipated theories to address these issues. Approaches of cloud robotics have developed from system-level to node-level. However, the present node-level systems are not flexible enough to dynamically adapt to changing conditions. To address this, we present ElasticROS, which evolves the present node-level systems into an algorithm-level one. ElasticROS is based on ROS and ROS2. For fog and cloud robotics, it is the first robot operating system with algorithm-level collaborative computing. ElasticROS develops elastic collaborative computing to achieve adaptability to dynamic conditions. The collaborative computing algorithm is the core and challenge of ElasticROS. We abstract the problem and then propose an algorithm named ElasAction to address. It is a dynamic action decision algorithm based on online learning, which determines how robots and servers cooperate. The algorithm dynamically updates parameters to adapt to changes of conditions where the robot is currently in. It achieves elastically distributing of computing tasks to robots and servers according to configurations. In addition, we prove that the regret upper bound of the ElasAction is sublinear, which guarantees its convergence and thus enables ElasticROS to be stable in its elasticity. Finally, we conducted experiments with ElasticROS on common tasks of robotics, including SLAM, grasping and human-robot dialogue, and then measured its performances in latency, CPU usage and power consumption. The algorithm-level ElasticROS performs significantly better than the present node-level system.
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