Optimization of shared autonomy vehicle control architectures for swarm operations

  • Authors:

  • Aaron J. Sengstacken;Daniel A. DeLaurentis;Mohammad R. Akbarzadeh-T

  • Affiliations:

  • Jet Propulsion Laboratory, Pasadena, CA and Department of Aeronautics and Astronautics, Purdue University, West Lafayette, IN;Department of Aeronautics and Astronautics, Purdue University, West Lafayette, IN;Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
  • Year:
  • 2010

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Abstract

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a "swarm" concept of operations. The swarm, which is a collection of vehicles traveling at high speeds and in close proximity, will require technology and management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared autonomy control approach, in which the strengths of both human drivers and machines are employed in concert for this management. Building from a fuzzy logic control implementation, optimal architectures for shared autonomy addressing differing classes of drivers (represented by the driver's response time) are developed through a genetic-algorithm-based search for preferred fuzzy rules. Additionally, a form of "phase transition" from a safe to an unsafe swarm architecture as the amount of sensor capability is varied uncovers key insights on the required technology to enable successful shared autonomy for swarm operations.