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A Compact Magnetic Field-Based Obstacle Detection and Avoidance System for Miniature Spherical Robots
Journal article   Peer reviewed

A Compact Magnetic Field-Based Obstacle Detection and Avoidance System for Miniature Spherical Robots

Fang Wu, Akash Vibhute, Gim Song Soh, Kristin L. Wood and Shaohui Foong
Sensors (Basel, Switzerland), Vol.17(6), p.1231
28/05/2017
PMID: 28555030

Abstract

Chemistry Chemistry, Analytical Engineering Engineering, Electrical & Electronic Instruments & Instrumentation Physical Sciences Science & Technology Technology
Due to their efficient locomotion and natural tolerance to hazardous environments, spherical robots have wide applications in security surveillance, exploration of unknown territory and emergency response. Numerous studies have been conducted on the driving mechanism, motion planning and trajectory tracking methods of spherical robots, yet very limited studies have been conducted regarding the obstacle avoidance capability of spherical robots. Most of the existing spherical robots rely on the "hit and run" technique, which has been argued to be a reasonable strategy because spherical robots have an inherent ability to recover from collisions. Without protruding components, they will not become stuck and can simply roll back after running into bstacles. However, for small scale spherical robots that contain sensitive surveillance sensors and cannot afford to utilize heavy protective shells, the absence of obstacle avoidance solutions would leave the robot at the mercy of potentially dangerous obstacles. In this paper, a compact magnetic field-based obstacle detection and avoidance system has been developed for miniature spherical robots. It utilizes a passive magnetic field so that the system is both compact and power efficient. The proposed system can detect not only the presence, but also the approaching direction of a ferromagnetic obstacle, therefore, an intelligent avoidance behavior can be generated by adapting the trajectory tracking method with the detection information. Design optimization is conducted to enhance the obstacle detection performance and detailed avoidance strategies are devised. Experimental results are also presented for validation purposes.
url
https://doi.org/10.3390/s17061231View
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