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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On this page
  • Problem Statement
  • Proposal For IMAV2019
  • Software Stack

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  1. Competitions

IMAV 2019, Madrid, Spain

This page contains codes and resources for the IMAV 2019 Outdoor Challenge

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Last updated 1 year ago

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IMAV 2019 was the 11th International Micro Air Vehicle Competition and Conference, held from 30th October to 4th November in Madrid, Spain.

Problem Statement

Proposal For IMAV2019

This contains the approach proposed by the Team for the Outdoor Challenge before the competition.

Software Stack

The complete software stack is available on Github. It contains all the modules we have used:

General Overview of the modules used:

  • Planner: A Finite State Machine implementation using the Boost C++ libraries for decision making, state transitions and actions during the mission.

  • Detector: A detection and pose estimation module to detect the colored mailboxes in the field.

  • Helipad Detector: A Helipad Detection module for accurate and precise landing on a helipad.

  • Collision Avoidance: A collision avoidance module for a multi-UAV system.

  • Feature Detector: A feature detection module for detection of a house roof and a crashed UAV.

Router: A message reception, checks and feedback system for keeping track of the detected mailboxes between the UAVs. Implemented with help of the package used to sync messages among the UAVs.

multimaster_fkie
https://github.com/AerialRobotics-IITK/imav2019
7MB
IMAV PS.pdf
pdf
IMAV 2019 Competition Rules
5MB
imav_report.pdf
pdf
Team Aerial Robotics Proposal