Aerial Robotics IITK
  • Introduction
  • Danger Zone
  • Tutorials
    • Workspace Setup
      • Installing Ubuntu
      • Basic Linux Setup
      • Spruce up your space
      • ROS Setup
      • PX4 Setup
        • PX4 Toolchain Setup
      • Ardupilot Setup
      • Installing Ground Control Station
        • QGroundControl
        • Mission Planner
      • ArduPilot Setup on Docker
      • PX4 Setup on Docker
    • How to Write a ROS Package
      • ROS Package
      • Node Handles, Parameters, and Topics
      • Coding Standards
      • Custom mavros message
      • Transformations
      • Conversions
    • Cheatsheets
      • CMakeCheatsheet
      • GitCheatsheet
      • LatexCheatsheet
      • Markdown Cheatsheet
    • Miscellaneous
      • Odroid XU4 Setup
      • Simulation using Offboard Control
        • Enable Offboard Mode in PX4
      • Writing a UDev rule
      • Sensor fusion
    • Reference wiki links
  • Concepts
    • Quaternions
      • Theory
    • Kalman Filters
    • Rotations
    • Path Planning
      • Grassfire Algorithm
      • Dijkstra Algorithm
      • A* Algorithm
      • Probabilistic Roadmap
      • RRT Algorithm
      • Visibility Graph Analysis
    • Lectures
      • Aerial Robotics
      • Avionics
      • Control Systems: Introduction
      • Control Systems: Models
      • Inter IIT Tech Meet 2018
      • Kalman Filters
      • Linux and Git
      • Git Tutorial
      • ROS
      • Rotorcraft
      • Software Training
  • Control System
    • Model Predictive Control
      • System Identification
      • Sample SysId Launch Files
      • Running MPC
        • MPC with Rotors
        • MPC with PX4 Sim
        • MPC with ROS
      • References
    • PID Controller
      • Introduction
      • Basic Theory
  • Estimation
    • Visual-Inertial Odometry
      • Hardware Requirements
      • Visual-Inertial Sensing
      • DIYing a VI-Sensor
    • Setup with VICON
    • Odometry from pose data
  • Computer Vision
    • Intel RealSense D435i setup for ROS Noetic
    • IntelRealSense D435i Calibration
    • Camera Calibration
    • ArUco ROS
  • Machine Learning
    • Datasets
  • Hardware Integration
    • Configuring Radio Telemetry
    • Setting up RTK + GPS
    • Integration of Sensors with PixHawk
      • Connecting Lidar-lite through I2C
    • Connections
    • Setting up Offboard Mission
      • Setting up Companion Computer
        • Raspberry Pi 4B Setup
        • Jetson TX2 Setup
      • Communication Setup
      • Guided mode
    • Miscellaneous
  • Resources
    • Open-source algorithms and resources
    • Courses
      • State Space Modelling of a Multirotor
      • Path Planning Lecture
      • Introduction to AI in Robotics
      • RRT, RRT* and RRT*- Path Planning Algorithms
    • Useful Reading Links
      • Aerial Robotics
      • Books
      • Computer Vision and Image Processing
      • Courses on AI and Robotics
      • Deep Neural Network
      • Dynamics and Controls system
      • Motion Planning
      • Probabilistic Robotics
      • Programming
      • Robotics Hardware
      • Miscellaneous and Awesome
    • Online Purchase websites
  • Competitions
    • Inter-IIT TechMeet 8.0
    • Inter-IIT TechMeet 9.0
    • IMAV 2019, Madrid, Spain
    • Inter-IIT TechMeet 10.0
    • Inter-IIT TechMeet 11.0
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  1. Concepts
  2. Path Planning

Dijkstra Algorithm

Around the world in 80 days, maybe less.

PreviousGrassfire AlgorithmNextA* Algorithm

Last updated 5 years ago

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The start and destination nodes, along with many intermediate nodes like B,C,D are considered for path planning. Going along a path from one node to other carries a cost along with it. The algorithm aims to minimise this cost while going from Start to End.

The computational complexity for this algorithm increases quadratically with the number of nodes.

For more information read:

LogoDijkstra's algorithmWikipedia