Real-Time Support for Mobile Robotics

Abstract

Coordinated behavior of mobile robots is an important emerging application area. Different coordinated behaviors can be achieved by assigning sets of control tasks, or strategies, to robots in a team. These control tasks must be scheduled either locally on the robot or distributed across the team. An application may have many control strategies to dynamically choose from, although some may not be feasible, given limited resource and time availability. Thus, dynamic feasibility checking becomes important as the coordination between robots and the tasks that need to be performed evolves with time. This paper presents an online algorithm for finding a feasible strategy given a functionally equivalent set of strategies for achieving an application's goals. We present two heuristics for feasibility checking. Both consider communication cost and utilization bound to make allocation (of tasks to execution sites) and scheduling decisions. Extensive experimental results show the effectiveness of the approaches, especially in resource-tight environments. We also demonstrate the application of our approach to real-world scenarios involving teams of robots and show how feasibility analysis also allows the prediction of scalability of the solution to large robot teams.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA438794

Entities

People

  • Huan Li
  • John Sweeney
  • Krithi Ramamritham
  • Prashant Shenoy
  • Roderic Grupen

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Case Studies
  • Communication Networks
  • Computations
  • Computer Science
  • Consumers
  • Embedded Systems
  • Environment
  • Information Operations
  • Multiple Access
  • Networks
  • Robotics
  • Robots
  • Scheduling (Production)
  • Simulations
  • Workload

Fields of Study

  • Computer science
  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control