Abstract
In order to broaden the use of microrobots in practical fields, autonomous control algorithms such as obstacle avoidance must be further developed. However, most previous studies of microrobots used manual motion control to navigate past tight spaces and obstacles while very few studies demonstrated the use of autonomous motion. In this paper, we demonstrated a dynamic obstacle avoidance algorithm for bacteria-powered microrobots (BPMs) using electric field in fluidic environments. A BPM consists of an artificial body, which is made of SU-8, and a high dense layer of harnessed bacteria. BPMs can be controlled using externally applied electric fields due to the electrokinetic property of bacteria. For developing dynamic obstacle avoidance for BPMs, a kinematic model of BPMs was utilized to prevent collision and a finite element model was used to characteristic the deformation of an electric field near the obstacle walls. In order to avoid fast moving obstacles, we modified our previously static obstacle avoidance approach using a modified vector field histogram (VFH) method. To validate the advanced algorithm in experiments, magnetically controlled moving obstacles were used to intercept the BPMs as the BPMs move from the initial position to final position. The algorithm was able to successfully guide the BPMs to reach their respective goal positions while avoiding the dynamic obstacles.
Highlights
It is well established that microscopic scale robotics has a high potential to be utilized in biological, medical, and industrial applications; despite facing many challenges
We demonstrated our suggested obstacle avoidance approach in the environment where there was one moving obstacle
The dynamic obstacle avoidance approach was demonstrated by successfully guiding bacteria-powered microrobots (BPMs) to avoid multiple nickel coated dynamic obstacles
Summary
It is well established that microscopic scale robotics has a high potential to be utilized in biological, medical, and industrial applications; despite facing many challenges. Core tasks such as localized/targeted drug delivery, micro invasive surgery, cell manipulation, biosensing, cell sorting, and cell fusion can be performed [1,2,3,4,5,6]. In the field of industrial engineering, microrobots have shown their capabilities to complete microscale tasks such as micro-assembly, transport, precision micro-machining, and micro-manipulation [7,8,9]. In order to develop swimming microrobots for these applications, there have been various challenges. The major challenge is to propel microrobots at a low Reynolds number fluidic.
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