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Drug Delivery Based on Swarm Microrobots

Computational Intelligence Research Laboratory (CIRLab), Department of Computer Engineering, Faculty of Engineering at Sriracha, Kasetsart University Sriracha Campus, Chonburi 20230, Thailand

E-mail Address: ananb@ieee.org

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Tiranee Achalakul

Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand

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, and

Romesh C Batra

Department of Engineering Science and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

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https://doi.org/10.1142/S1469026816500061Cited by:5 (Source: Crossref)

Abstract

Advances in the development of technology have led to microrobots applications in medical fields. Drug delivery is one of these applications in which microrobots deliver a pharmaceutical compound to targeted cells. Chemotherapy and its side effects can then be minimized by this method. Two major constraints, however, must be considered: the robot’s onboard energy supply and the time needed for drug delivery. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work were to find optimal paths to targeted cells and avoid collision with obstacles in the paths under a dynamic environment. In this study, we controlled motion of microrobots based on the concept of swarm intelligence. Artificial Bee Colony (ABC), the Best-so-far ABC, and the Particle Swarm Optimization (PSO) methods were employed to implement the collision detection and the boundary distance detection modules. Forces that drove or resisted blood flow as well as pressure in blood vessels were considered to approximate the effects of the environment on the microrobots. Numerical experiments were conducted using various obstacle environments. The results confirm that the proposed approaches were successful in avoiding obstacles and optimizing the energy consumption used to reach the target.

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References

1. A. Cavalcanti and R. A. Freitas, Nanorobotics control design: A collective behavior approach for medicine, IEEE Trans. Nanobiosci. 4 (2005) 133–140. Crossref, Google Scholar
2. G. M. Patel, G. C. Patel, R. B. Patel, J. K. Patel and M. Patel, Nanorobot: A versatile tool in nanomedicine, J. Drug Target. 14 (2006) 63–67. Crossref, Google Scholar
3. A. Cavalcanti, B. Shirinzadeh, R. A. Freitas and T. Hogg, Nanorobot architecture for medical target identification, Nanotechnology 19 (2008) 1–12. Crossref, Google Scholar
4. P. Dario, M. C. Carrozza, L. Lencioni, B. Magnani and S. D’Attanasio, Micro robotic system for colonoscopy, Proc. IEEE Int. Conf. Robotics and Automation (1997) pp. 1567–1572. Google Scholar
5. K. Ishiyama, M. Sendoh and K. I. Arai, Magnetic micromachines for medical applications, J. Magn. Magn. Mater. 242–245 (2002) 41–46. Crossref, Google Scholar
6. C. Yu, J. Kim, H. Choi, J. Choi, S. Jeong, K. Cha, J.-O. Park and S. Park, Novel electromagnetic actuation system for three-dimensional locomotion and drilling of intravascular microrobot, Sens. Actuators A: Phys. 161 (2010) 296–304. Crossref, Google Scholar
7. B. J. Nelson, I. K. Kaliakatsos and J. J. Abbott, Microrobots for minimally invasive medicine, Annu. Rev. Biomed. Eng. 12 (2010) 55–85. Crossref, Google Scholar
8. A. Stentz, Optimal and efficient path planning for partially-known environments, Proc. IEEE Int. Conf. Robotics and Automation (1994) pp. 3310–3317. Google Scholar
9. M. S. G. Tsuzuki, T. C. Martins and F. K. Takase, Robot path planning using simulated annealing, 12th IFAC Symp. Information Control Problems in Manufacturing (2006) pp. 173–178. Google Scholar
10. W. M. Tao and M. Zhang, A genetic algorithm-based area coverage approach for controlled drug delivery using microrobots, Nanomedicine 1 (2005) 91–100. Crossref, Google Scholar
11. M. Brand, M. Masuda, N. Wehner and X.-H. Yu, Ant colony optimization algorithm for robot path planning, Proc. 2010 Int. Conf. Computer Design and Applications (2010) pp. 436–440. Google Scholar
12. N. Ganganath, C.-T. Cheng and C. K. Tse, An ACO-based off-line path planner for nonholonomic mobile robots, Proc. IEEE Int. Symp. Circuits and Systems (2014) pp. 1038–1041. Google Scholar
13. Y. Q. Qin, D. B. Sun, M. Li and Y. G. Cen, Path planning for mobile robot using the particle swarm optimization with mutation operator, Proc. Int. Conf. Machine Learning and Cybernetics Vol. 4 (2004) pp. 2473–2478. Google Scholar
14. X. Chen and Y. Li, Smooth path planning of a mobile robot using stochastic particle swarm optimization, Proc. IEEE on Mechatronics and Automation (2006) pp. 1722–1727. Google Scholar
15. Q. Zhang and S. Li, A global path planning approach based on particle swarm optimization for a mobile robot, Proc. 7th WSEAS Int. Conf. Robotics, Control, and Manufacturing Technology (2007) pp. 263–267. Google Scholar
16. K. H. S. Hla, Y. Choi and J. S. Park, Obstacle avoidance algorithm for collective movement in nanorobots, Int. J. Comput. Sci. Netw. Secur. 8 (2008) 302–309. Google Scholar
17. Y. Zhang, D.-W. Gong and J.-H. Zhang, Robot path planning in uncertain environment using multi-objective particle swarm optimization, Neurocomputing 103 (2013) 172–185. Crossref, Google Scholar
18. B. B. V. L. Deepak, D. R. Parhi and B. M. V. A. Raju, Advance particle swarm optimization-based navigational controller for mobile robot, Arab. J. Sci. Eng. 39 (2014) 6477–6487. Crossref, Google Scholar
19. S. Chandrasekarn and D. F. Hougen, Swarm intelligence for cooperation of bio-nano robots using quorum sensing, Proc. BioMicro and Nanosystems Conf. (2006) pp. 15–18. Google Scholar
20. Md. A. Hossain and I. Ferdous, Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique, Robot. Auton. Syst. 64 (2015) 137–141. Crossref, Google Scholar
21. P. K. Mohanty and D. R. Parhi, Cuckoo search algorithm for the mobile robot navigation, Proc. 4th Int. Conf. Swarm, Evolutionary and Memetic Computing (2013) pp. 527–536. Google Scholar
22. A. Banharnsakun, T. Achalakul and R. C. Batra, Target finding and obstacle avoidance algorithm for microrobot swarms, Proc. 2012 IEEE Int. Conf. Systems, Man, and Cybernetics (2012) pp. 1610–1615. Google Scholar
23. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Computer Engineering Department, Erciyes University, Turkey, 2005. Google Scholar
24. A. Banharnsakun, T. Achalakul and B. Sirinaovakul, The best-so-far selection in artificial bee colony algorithm, Appl. Soft Comput. 11 (2011) 2888–2901. Crossref, Google Scholar
25. J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proc. 1995 IEEE Int. Conf. Neural Networks Vol. 4 (1995) pp. 1942–1948. Google Scholar
26. Y.C. Fung, Biomechanics: Circulation, 2nd edn. (Springer, New York, 1996). Google Scholar
27. J. B. Franzini and E. J. Finnemore, Fluid Mechanics, 9th edn. (McGraw-Hill, New York, 1997). Google Scholar
28. K. B. Yesin, K. Vollmers and B. J. Nelson, Modeling and control of untethered biomicrorobots in a fluidic environment using electromagnetic fields, Int. J. Robot. Res. 25 (2006) 527–536. Crossref, Google Scholar
29. R. K. Soong, G. D. Bachand, H. P. Neves, A. G. Olkhovets, H. G. Craighead and C. D. Montemagno, Powering an inorganic nanodevice with a biomolecular motor, Science 290 (2000) 1555–1558. Crossref, Google Scholar
30. S. Takeuchi and I. Shimoyama, Selective drive of electrostatic actuators using remote inductive powering, Sens. Actuators A 95 (2002) 269–273. Crossref, Google Scholar
31. C. Sauer, M. Stanacevic, G. Cauwenberghs and N. Thakor, Power harvesting and telemetry in CMOS for implanted devices, IEEE Trans. Circuits Syst. 52 (2005) 2605–2613. Crossref, Google Scholar
32. H. Aubert, RFID technology for human implant devices, C. R. Phys. 12 (2011) 675–683. Crossref, Google Scholar
33. L. Rubinstein, A practical nanorobot for treatment of various medical problems, Proc. 8th Foresight Conf. Molecular Nanotechnology Vol. 2 (2002) pp. 208–214. Google Scholar
34. J. C. Biesmeijer and T. D. Seeley, The use of waggle dance information by honey bees throughout their foraging careers, Behav. Ecol. Sociobiol. 59 (2005) 133–142. Crossref, Google Scholar
35. D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, J. Glob. Optim. 39 (2007) 459–471. Crossref, Google Scholar
36. A. Banharnsakun, T. Achalakul and B. Sirinaovakul, Job shop scheduling with the best-so-far ABC, Eng. Appl. Artif. Intell. 25 (2012) 583–593. Crossref, Google Scholar
37. A. Banharnsakun, B. Sirinaovakul and T. Achalakul, The best-so-far ABC with multiple patrilines for clustering problems, Neurocomputing 116 (2013) 355–366. Crossref, Google Scholar
38. A. Banharnsakun, B. Sirinaovakul and T. Achalakul, The performance and sensitivity of the parameters setting on the best-so-far ABC, The Ninth Int. Conf. Simulated Evolution and Learning eds. L. T. Bui et al., (Springer, Berlin, 2012) pp. 248–257. Google Scholar
39. P. E. Hart, N. J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern. 4 (1968) 100–107. Crossref, Google Scholar
40. J. Yao, C. Lin, X. Xie, A. J. Wang and C. C. Hung, Path planning for virtual human motion using improved A* algorithm, Proc. Seventh Int. Conf. Information Technology: New Generations (2010) pp. 1154–1158. Google Scholar
41. J. Cutnell and K. Johnson, Physics, 4th edn. (Wiley, California, 1998). Google Scholar
42. R. Glaser, Biophysics, 4th edn. (Springer, New York, 2001). Google Scholar
43. K. B. Chandran, A. P. Yoganathan and S. E. Rittgers, Biofluid Mechanics: The Human Circulation (CRC/Taylor and Francis, Boca Raton, 2007). Google Scholar