Analog Twin Framework for Human and AI Supervisory Control and Teleoperation of Robots

Abstract

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.

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

“…• Digital twin models in Cyber-Physical Manufacturing Systems (CPMS) [73], [74] • Analog Twin (AT) Framework for Human and AI Supervisory Control [75] • Experimentation in a remote laboratory setting [76] • Multi-robot localization and mapping [23], [77], [78] • Learning from observation [25] • Mobile manipulator positioning for object pick-up [26] • Navigation and obstacle avoidance [35], [36], [79]- [85] • Autonomous exploration in indoor environments [30], [86]- [88] • Immersive telepresence [89] • The specific application was not mentioned [27]- [29], [32], [53], [90]-[95] Healthcare…”

“…PeopleBot [42], [91] Pioneer 3-DX [19], [36], [42], [72], [119] Clearpath Robotics Husky [28], [75], [85], [116], [174], [175], [178] Clearpath Robotics Jackal [18], [28], [53], [94] Willow Garage TurtleBot series [2], [8], [9], [12], [25], [30], [35], [70], [81], [88], [100], [105], [143], [144], [147], [148], [150], [158], [178]- [185] Willow Garage PR2 [87], [124] Roomba vacuum cleaner [86], [172] Eddie [186] Rob@work 3 [187] KUKA YouBot [138], [142] Arlobot [134] PlatypOUs [137] Ceres (Volksbot platform)…”

A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

“…In our previous work [1], we proposed an analog twin (AT) framework by synchronizing mobility between two mobile robots, where one robot acts as an AT to the other robot. A priority-based supervised bilateral teleoperation strategy is used to offload part of the robotic tasks to the AT without saturating the network conditions.…”

“…Offloading enables robot operators to access data from anywhere at any time by providing global storage as well as processing. Furthermore, given the data is maintained in the cloud, robots may be monitored and controlled remotely [1], facilitating the ability to introduce Cyber-Physical Systems (CPS) that require automated actions in the sense-compute-actuate cycle [2]. The dynamic support of CPS is strongly reliant on the timely delivery of the appropriate data placement to the right computing entity [3].…”

Sensory Manipulation as a Countermeasure to Robot Teleoperation Delays: System and Evidence

“…Parallelly, the integration of supervisory controls has been explored as a robust solution to mitigate the multifaceted impacts of teleoperation delays 6,12,13 . This modality allows for an intelligent delegation of control tasks to the autonomous subsystems within robots, reducing the necessity for continuous manual input and mitigating the adverse impacts of delays on operator workload and task e cacy 45 . Furthermore, adaptive and robust control strategies have been at the forefront of mitigative research, focusing on maintaining system stability and performance optimization amidst varying operational conditions by dynamically adjusting control parameters in alignment with observed system states and delay magnitudes 18 .…”