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Published on: January 3, 2011
A Mosquito Pick-and-Place System for PfSPZ-based Malaria Vaccine Production
Henry Phalen1, Prasad Vagdargi1, Mariah L Schrum1
1H. Phalen, P. Vagdargi, G. S. Chirikjian, I. Iordachita, and R. H. Taylor are with the Laboratory for Computational Sensing and Robotics (LCSR) at the Johns Hopkins University in Baltimore, MD, USA. Previously, M. L. Schrum, A. Canezin, M. Pozin, and S. Coemert were also with the LCSR. Now, M. L. Schrum is with Georgia Tech, A. Canezin is with Accenture, M. Pozin is with Auris Health, and S. Coemert is with the Technical University of Munich. G. S. Chirikjian is currently at the National University of Singapore, Department of Mechanical Engineering, 117575, Singapore. S. Chakravarty and S. L. Hoffman are with Sanaria, Inc. in Rockville, MD, USA.
Abstract:
The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task.
Note To Practitioners—:
Automated processes could help increase malaria vaccine production to global scale. Currently, production requires technicians to manually dissect mosquitoes, a process that is slow, tedious, and requires a lengthy training regimen. This paper presents an an improved manual fixture and procedure that reduces technician training time. Further, an approach to automate this dissection process is proposed and the critical step of robotic manipulation of the mosquito with the aid of computer vision is demonstrated. Our approach may serve as a useful example of system design and integration for practitioners that seek to perform new and challenging pick-and-place tasks with small, non-uniform, and highly deformable objects.
