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Related Experiment Video

Updated: Nov 12, 2025

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
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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.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|March 22, 2021
PubMed
Summary

Automating malaria vaccine production involves a new robotic system for dissecting mosquitoes. This technology improves efficiency and reduces training time for producing Plasmodium falciparum sporozoites (PfSPZ) vaccines.

Keywords:
Biomedical engineeeringBiomedical imagingManufacturing automationRobot vision systemsRobots

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Area of Science:

  • Biotechnology
  • Robotics
  • Parasitology

Background:

  • Malaria vaccine production is crucial for global health.
  • Current manual dissection of Plasmodium falciparum sporozoites (PfSPZ) is inefficient for large-scale vaccine manufacturing.
  • Manual dissection requires extensive technician training and is time-consuming.

Purpose of the Study:

  • To develop an improved manual dissection procedure and fixture to reduce training time.
  • To demonstrate the automation of mosquito dissection for large-scale malaria vaccine production.
  • To present a robotic pick-and-place system guided by computer vision for handling mosquitoes.

Main Methods:

  • An improved manual dissection fixture and procedure were developed.
  • A robotic system with a custom micro-gripper and a 4-DOF robot was designed.
  • A computer vision system guided the robotic arm for autonomous mosquito grasping and placement.
  • Mosquitoes were placed into dissection blades for head removal to access salivary glands.

Main Results:

  • The improved manual procedure reduced technician training time.
  • The robotic system achieved 100% grasping accuracy in a pilot test with 50 mosquitoes.
  • The system demonstrated 90% accuracy in placing mosquitoes for head dissection.

Conclusions:

  • Automated dissection offers a promising solution for scaling up malaria vaccine production.
  • The robotic system demonstrates feasibility for challenging pick-and-place tasks involving small, deformable objects.
  • This approach can serve as a model for integrating robotics and computer vision in specialized manufacturing processes.