Optimal and Efficient Path Planning for Unknown and Dynamic Environments

Abstract

The task of planning trajectories for a mobile robot has received considerable attention in the research literature. Algorithms exist for handling a variety of robot shapes, configurations, motion constraints, and environments. Most of the work assumes the robot has a complete and accurate model of its environment before it begins to move; less attention has been paid to the problem of unknown or partially-known environments. This situation occurs for an exploratory robot or one that must move to a goal location without the benefit of a floorplan (indoor) or terrain map (outdoor). Existing approaches plan an initial global path or route based on known information and then modify the plan locally as the robot discovers obstacles with its sensors. While this strategy works well in environments with small, sparse obstacles, it can lead to grossly suboptimal and incomplete results in cluttered spaces. An alternative approach is to replan the global path from scratch each time a new obstacle is discovered.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1993
Accession Number
ADA273871

Entities

People

  • Anthony Stentz

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Cells
  • Collision Avoidance
  • Continuous Spectra
  • Cost Reductions
  • Costs
  • Dead Reckoning
  • Environment
  • Ground Vehicles
  • Laser Rangefinding
  • Motion Planning
  • Navigation
  • Robots
  • Sequences
  • Shape
  • Trajectories
  • Unmanned Ground Vehicles

Fields of Study

  • Computer science

Readers

  • Educational Psychology
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers