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Mobile Robot Control and Autonomy Through Collaborative Simulation Twin

2023-03-10 19:15:51
Nazish Tahir, Ramviyas Parasuraman


When a mobile robot lacks high onboard computing or networking capabilities, it can rely on remote computing architecture for its control and autonomy. This paper introduces a novel collaborative Simulation Twin (ST) strategy for control and autonomy on resource-constrained robots. The practical implementation of such a strategy entails a mobile robot system divided into a cyber (simulated) and physical (real) space separated over a communication channel where the physical robot resides on the site of operation guided by a simulated autonomous agent from a remote location maintained over a network. Building on top of the digital twin concept, our collaborative twin is capable of autonomous navigation through an advanced SLAM-based path planning algorithm, while the physical robot is capable of tracking the Simulated twin's velocity and communicating feedback generated through interaction with its environment. We proposed a prioritized path planning application to the test in a collaborative teleoperation system of a physical robot guided by ST's autonomous navigation. We examine the performance of a physical robot led by autonomous navigation from the Collaborative Twin and assisted by a predicted force received from the physical robot. The experimental findings indicate the practicality of the proposed simulation-physical twinning approach and provide computational and network performance improvements compared to typical remote computing (or offloading), and digital twin approaches.

Abstract (translated)

当移动机器人缺乏高性能内置计算或网络能力时,它可以利用远程计算架构对其控制和自主进行依赖。本文介绍了一种针对资源受限机器人的控制和自主的新型合作模拟双头(ST)策略。这种策略的实际实施包括将移动机器人系统划分为 cyber(模拟)和物理(实际)空间,在通信通道上分离,其中物理机器人位于操作site由远程维护的模拟自主代理引导的一个模拟位置。在数字双胞胎概念的基础上,我们的合作双头可以通过先进的 SLAM 路径规划算法自主导航,而物理机器人可以跟踪模拟双头的速度并产生与环境互动产生的交流反馈。我们提出了一种优先路径规划应用,用于测试由 ST 的自主导航引导的物理机器人的协作远程操作系统。我们检查了由合作双头领导的物理机器人的表现,并借助从物理机器人接收的预测力进行协助。实验结果表明,提出的模拟物理孪生方法的实际可行性,并相比典型的远程计算(或卸载)和数字孪生方法提供了计算和网络性能改进。



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