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A Multi-objective Genetic Algorithm for Path Planning with Micro Aerial Vehicle Test Bed

  • H. David Mathias
  • Vincent R. Ragusa
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)

Abstract

The problem of robotic path planning is relevant to many applications that have led to extensive study. This has only increased as autonomous robotic vehicles have become more affordable and varied. Optimal solutions to this problem can be computationally expensive, leading to the need for efficiently achievable approximate solutions. In this work, we present a genetic algorithm to solve the path planning problem. The algorithm operates offline but runs onboard the micro aerial vehicle (MAV). This is accomplished by mounting a single-board computer on the vehicle and integrating it with the flight control board. In addition, we evaluate the effectiveness of two genetic operators: crossover and mass extinction. Results demonstrate that a standard, single-point crossover operator is largely ineffective. Mass extinction, an operator that has been used rarely in previous work, is explored within the framework of a genetic algorithm utilizing only mutation and selection. Based on initial results, mass extinction may have some utility for path planning, however, due to large number of parameters and potential implementations, additional experimentation is needed.

Notes

Acknowledgement

The authors thank Florida Southern College for financial support of this work. They also thank Annie Wu for valuable insights and feedback, Susan Serrano and Isabel Loyd for assistance with the statistical analysis, and members of the ArduPilot and Dronekit-Python development teams for valuable suggestions for developing flight control client code.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Florida Southern CollegeLakelandUSA

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