Planning with Uncertainty in Position: an Optimal Planner

Abstract

We propose a resolution-optimal planner that considers uncertainty while optimizing any monotonic objective function such as mobility cost, risk, energy expended, etc. The resulting path is a one that minimizes the expected cost value of the objective function, while ensuring that the uncertainty in the position of the robot does not compromise the safety of the robot or the reachability of the goal.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2004
Accession Number
ADA526163

Entities

People

  • Anthony Stentz
  • Juan P. Gonzalez

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Cells
  • Computational Complexity
  • Gaussian Distributions
  • Global Positioning Systems
  • Kalman Filters
  • Mobility
  • Motion Planning
  • Navigation
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Robots
  • Three Dimensional
  • Two Dimensional
  • Uncertainty

Fields of Study

  • Physics

Readers

  • Life Cycle Cost Analysis
  • Operations Research
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy