Observability-based Local Path Planning and Collision Avoidance Using Bearing-only Measurements

Abstract

Small and Miniature Air Vehicles (MAVs) have the potential to perform tasks that are too difficult or dangerous for human pilots. For example, they can monitor critical infrastructure and real-time disasters, perform search and rescue, and measure weather in-storms [1]. For many of these applications, MAVs are required to navigate in urban or unknown terrain where obstacles of various types and sizes may hinder the success of the mission. MAVs must have the capability to autonomously plan paths that do not collide with buildings, trees or other obstacles. Therefore, the path planning and obstacle avoidance problems for MAVs have received significant attention [1, 2, 3, 4, 5].

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 20, 2012
Accession Number
ADA587498

Entities

People

  • Clark N. Taylor
  • Huili Yu
  • Rajnikant Sharma
  • Randal W. Beard

Organizations

  • Brigham Young University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Collision Avoidance
  • Coordinate Systems
  • Covariance
  • Data Science
  • Environment
  • Errors
  • Guarantees
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Measurement
  • Monte Carlo Method
  • Motion Planning
  • Simulations
  • Statistical Algorithms

Readers

  • Emergency Management and Homeland Security.
  • Robotics and Automation.