Robot Path Planning in Uncertain Environments: A Language Measure-theoretic Approach

Abstract

This paper addresses the problem of goal-directed robot path-planning in the presence of uncertainties that are induced by bounded environmental disturbances and actuation errors. The offline infinite-horizon optimal plan is locally updated by online finite-horizon adaptive re-planning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a gridbased path planning algorithm, called v*, that was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control-theoretic perspective. The proposed concept has been validated on a simulation test bed that is constructed upon a model of typical autonomous underwater vehicles (AUVs) in the presence of uncertainties.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA602676

Entities

People

  • Asok R. Fellow
  • Devesh K. Jha
  • Thomas Wettergren
  • Yue Li

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Automata
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Underwater Vehicles
  • Autonomous Vehicles
  • Collision Avoidance
  • Language
  • Motion Planning
  • Robots
  • Simulations
  • Test Beds
  • Underwater Vehicles
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles
  • Vehicles

Readers

  • Mathematical Modeling and Probability Theory.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy