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 replanning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a grid-based path planning algorithm, called v* , which 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
Mar 01, 2015
Accession Number
ADA621589

Entities

People

  • Asok Ray
  • Devesh K. Jha
  • Thomas Wettergren
  • Yue Li

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Underwater Vehicles
  • Collision Avoidance
  • Engineering
  • Environment
  • Language
  • Motion Planning
  • Navigation
  • Nuclear Engineering
  • Ocean Currents
  • Probability
  • Robots
  • Test Beds
  • Unmanned Aerial Vehicles

Readers

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

Technology Areas

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