An Information-Centric Approach to Autonomous Trajectory Planning Utilizing Optimal Control Techniques

Abstract

This work introduces a new information-centric pseudospectral optimal control-based algorithm for autonomous trajectory planning and control of unmanned ground vehicles with real-time information updates. It begins with a comprehensive study and comparison of the various path planning methods currently in use. It then provides an analysis of the optimal control method, including vehicle and obstacle modeling techniques, several different problem formulations, and a number of important insights on unmanned ground vehicle motion planning. The new algorithm is then utilized on a collection of motion planning scenarios with varying levels of information; the performance of the planner and the solution accuracies under these varying levels of information are studied for both single and multi-vehicle scenarios. The multi-vehicle scenarios compare and contrast centralized, decentralized, decoupled, coordinated, cooperative, and prioritized control methods. Finally, the versatility of the planner (and the optimal control technique) is demonstrated, as it is used as both a path follower and trajectory planner in a collection of scenarios, including multi-vehicle formations and sector keeping.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2009
Accession Number
ADA509422

Entities

People

  • Michael A. Hurni

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Underwater Vehicles
  • Collision Avoidance
  • Computational Complexity
  • Control Systems
  • Ground Vehicles
  • Guidance
  • Information Systems
  • Motion Planning
  • Robots
  • Unmanned Aerial Vehicles
  • Unmanned Ground Vehicles
  • Unmanned Systems
  • Unmanned Vehicles

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control