Conference proceeding
Dynamic Obstacle Avoidance for Bacteria-Powered Microrobots
2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), v 2015, pp 2000-2005
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
As microscale robots are becoming increasingly popular due to their potential for medical and industrial applications, various designs of microscale robotic system have been developed. However, there has not been much work on autonomous control algorithms for microscale robots in microfluidic environments. In this paper, we introduce an autonomous navigation algorithm for the bacteria-powered microrobots (BPMs) in a workspace with moving obstacles. A BPM consists of a rigid inorganic body with bacteria attached on the surface. The attached bacteria provide propulsive force and are controllable using electric fields, which had been demonstrated in previous work. We take the controllability of BPMs and the unpredictable motion of dynamic obstacles into account to develop a dynamic obstacle avoidance approach. Moreover, we use finite element simulation to observe an electric field around a moving obstacle to model the field's deformation. Demonstration of dynamic obstacle avoidance approach through simulation results and experimental data are presented in the paper.
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2 citations in Scopus
Details
- Title
- Dynamic Obstacle Avoidance for Bacteria-Powered Microrobots
- Creators
- Hoyeon Kim - Drexel UniversityU. Kei Cheang - Drexel UniversityA. Agung Julius - Rensselaer Polytechnic InstituteMin Jun Kim - Drexel University
- Publication Details
- 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), v 2015, pp 2000-2005
- Conference
- 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Hamburg, Germany, 28 Sep 2015–02 Oct 2015)
- Series
- IEEE International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000371885402025
- Scopus ID
- 2-s2.0-84958181498
- Other Identifier
- 991019173712304721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Robotics