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.

翻译：当移动机器人缺乏强大的机载计算或网络能力时，可依赖远程计算架构实现其控制与自主导航。本文提出一种新颖的协作式仿真孪生（Simulation Twin, ST）策略，用于资源受限机器人的控制与自主导航。该策略的实际实现需将移动机器人系统划分为通过通信信道分离的虚拟（仿真）空间与物理（真实）空间，其中物理机器人位于操作现场，由远程位置通过网络维持的仿真自主智能体引导。在数字孪生概念基础上，我们的协作孪生体能够通过基于先进SLAM的路径规划算法实现自主导航，而物理机器人则可追踪仿真孪生体的速度信息，并反馈与环境交互产生的信号。我们提出一种优先级路径规划应用，在由ST自主导航引导的物理机器人协作遥操作系统中进行测试，同时评估了物理机器人在协作孪生体自主导航引导下，结合其自身预测力反馈的性能表现。实验结果表明，所提出的仿真-物理孪生方法具有实用价值，相比传统远程计算（或任务卸载）及数字孪生方法，在计算性能与网络性能方面均有所提升。