Multiscale Dynamic Data Driven Guidance and Control for Autonomous Vehicle Networks

Abstract

This project investigates coupled sensor configuration and planning (CSCP) for autonomous systems, which is a mode of active control of information in context to a decision-making problem. We consider a scenario consisting of a network of mobile vehicles called sensors, and a separate network of mobile vehicles called actors. Sensors gather information about the environment whereas actors perform desired tasks. Specifically, the actors perform tasks encoded in terms of multi-vehicle route-planning problems in a threatening environment. The threat is an unknown spatiotemporally-varying scalar field that is estimated using observations made by sensors. The major accomplishments and successes of this project are as follows: 1. Multiple research contributions for the optimal placement of sensors to collect observations of the most relevance to the actors route-planning problem.

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Document Details

Document Type
Technical Report
Publication Date
Sep 08, 2021
Accession Number
AD1155198

Entities

People

  • Raghvendra V Cowlagi

Organizations

  • Worcester Polytechnic Institute

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Autonomous Systems
  • Cognitive Systems Engineering
  • Computational Complexity
  • Computational Science
  • Control Systems
  • Detectors
  • Guidance
  • Information Processing
  • Information Science
  • Mechanical Engineering
  • Motion Planning
  • Multiagent Systems
  • Robotic Swarms
  • Sensor Networks
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control