A genetic algorithm-based area coverage approach for controlled drug delivery using microrobots.
BACKGROUND
The use of microrobots for controlled drug delivery shows great potential to achieve precise targeting with controllable side effects. One of the major challenges for controlled drug delivery is robot path planning for area coverage.


METHODS
This article proposes a genetic algorithm (GA)-based area coverage approach for robot path planning. The GA-based area coverage approach is characterized by (1) online path planning combined with offline path planning to cope with environmental uncertainties and (2) optimal path planning for selecting an optimal path by evaluating path lengths and turning angles. The expandable chromosome concept is proposed and implemented for the area coverage.


RESULTS
Simulation results from 5 different map environments show that the proposed approach achieved significant improvement in path effectiveness compared with the fixed-path approach.


CONCLUSIONS
The proposed GA approach has advantages over traditional path planning approaches in terms of computational costs and has advantages over existing online path planning approaches (eg, fixed-path plan approaches or path-length optimization approaches) in terms of path optimality.
Tao
WeiMin
W
Brooks Automation Inc., Mountain View, California, USA.
Zhang
Mingjun
M
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D003692:Delayed-Action Preparations; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D015918:Infusion Pumps, Implantable; D008904:Miniaturization; D008954:Models, Biological; D050997:Nanomedicine / Q000379:methods*; D012371:Robotics / Q000379:methods*
D003692:Delayed-Action Preparations