Paper Reading AI Learner

Bacteria Inspired Multi-Flagella Propelled Soft Robot at Low Reynolds Number

2021-11-24 20:53:19
Sangmin Lim, Achyuta Yadunandan, Mohammad Khalid Jawed

Abstract

The locomotion and mechanical efficiency of micro organisms, specifically micro-swimmers, have drawn interest in the fields of biology and fluid dynamics. A challenge in designing flagellated micro- and macro-scale robots is the geometrically nonlinear deformation of slender structures (e.g. rod-like flagella) ensuing from the interplay of elasticity and hydrodynamics. Certain types of bacteria such as Escherichia coli propel themselves by rotating multiple filamentary structures in low Reynolds flow. This multi-flagellated propulsive mechanism is qualitatively different from the single-flagellated mechanism exhibited by some other types of bacteria such as Vibrio cholerae. The differences include the flagella forming a bundle to increase directional stability for cell motility, offering redundancy for a cell to move, and offering the ability of flagella to be the delivery material itself. Above all, multi-flagellated biological system can inspire novel soft robots for application in drug transportation and delivery within the human body. We present a macroscopic soft robotic hardware platform and a computational framework for a physically plausible simulation model of the multi-flagellated robot. The fluid-structure interaction simulation couples the Discrete Elastic Rods algorithm with the method of Regularized Stokeslet Segments. Contact between two flagella is handled by a penalty-based method due to Spillmann and Teschner. We present comparison between our experimental and simulation results and verify that the simulation tool can capture the essential physics of this problem. The stability and efficiency of a multi-flagellated robot are compared with the single-flagellated counterpart.

Abstract (translated)

URL

https://arxiv.org/abs/2111.12793

PDF

https://arxiv.org/pdf/2111.12793.pdf


Tags
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 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 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