论文标题
Elasticros：雾和云机器人技术的弹性协作机器人操作系统
ElasticROS: An Elastically Collaborative Robot Operation System for Fog and Cloud Robotics
论文作者
论文摘要
机器人正在集成更大尺寸的模型以丰富功能并提高准确性，从而导致无法控制的计算压力。因此，机器人在计算功率和电池容量中遇到瓶颈。雾或云机器人技术是解决这些问题的最期待的理论之一。云机器人技术的方法已从系统级到节点级别开发。但是，当前的节点级系统不够灵活，无法动态适应变化的条件。为了解决这个问题，我们提出了Elasticros，该Elasticros将当前的节点级系统演变为算法级别。 Elasticros基于ROS和ROS2。对于FOG和Cloud Robotics，它是第一个具有算法级协作计算的机器人操作系统。 Elasticros会开发弹性协作计算，以实现对动态条件的适应性。协作计算算法是Elasticros的核心和挑战。我们抽象问题，然后提出一种称为Elasaction的算法以解决。这是基于在线学习的动态行动决策算法，它决定了机器人和服务器的配合方式。该算法会动态更新参数，以适应机器人当前所在的条件的变化。它根据配置将计算任务的弹性分配到机器人和服务器上。此外，我们证明了弹性的遗憾上限是sublinear，它保证了其收敛性，因此使Elasticros在其弹性方面保持稳定。最后，我们对机器人技术的常见任务进行了Elasticros进行实验，包括SLAM，GRASPING和HUMAN-OBOT对话，然后在潜伏期，CPU使用和功率消耗中测量其性能。算法级弹性弹性的性能明显优于当前的节点级系统。
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.
