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.

CONCLUSION

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.