Scheduling with Group Dynamics: a Multi-Robot Task Allocation Algorithm based on Vacancy Chains

Abstract

Existing task allocation and scheduling algorithms, including task- allocation algorithms for multi-robot systems, generally assume that tasks are independent. This assumption is often violated in groups of cooperative mobile robots, where the group dynamics can have a critical impact on performance. We present a multi-robot task allocation algorithm that is sensitive to group dynamics. Our algorithm is based on vacancy chains, a resource distribution process common in human and animal societies. We study the problem of cooperative transportation in simulation. We demonstrate through experiments in simulation that if robots keep local task utility estimates, and follow a greedy task selection policy, the interactions in the group cause the collection of learned policies to converge toward an optimal allocation pattern as defined by the vacancy chain framework. As the robots are continuously updating their individual utility estimates, the vacancy chain algorithm has the additional property of adapting automatically to changes in the environment, e.g., robot breakdowns or changes in task values. Our experiments show that in the case of such changes, the vacancy chain algorithm consistently outperforms random and static task allocation algorithms. Finally, the vacancy chain algorithm uses no communication or unique roles, and as a result it is more likely to scale to large groups and will degrade gracefully in response to individual breakdowns.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA462165

Entities

People

  • Gaurav S. Sukhatme
  • Maja Matarić
  • Torbjorn S. Dahl

Organizations

  • University of Southern California

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Collision Avoidance
  • Computational Complexity
  • Computational Science
  • Computer Science
  • Control Systems
  • Differential Equations
  • Dynamics
  • Equations
  • Game Theory
  • Group Dynamics
  • Machine Learning
  • Reinforcement Learning
  • Robotic Swarms
  • Scheduling (Production)
  • Self Organizing Systems

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Materials Science and Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - Human-Robot Interaction