Paper Reading AI Learner

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。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot