Enhanced Navigation Algorithm for UAVs and Mobile Robots using Low-Grade GPS and IMU Modules
Disciplines
Robotics
Abstract (300 words maximum)
Global Positioning Systems (GPS) have been widely used for mobile robot and unmanned aerial vehicle navigation for localization of the respective robot positions. This is often integrated with Inertial Measurement Units (IMU) for accurate positioning and odometry. A common navigation algorithm used to integrate these two sensors is using previously obtained velocity values and heading direction from IMU to calculate the current position, also known as dead reckoning, and fusing it with GPS data to calibrate the calculations and fix unexpected position errors. Such methods are simple to implement but are highly vulnerable to errors in environments with low satellite connectivity and thus, require high-grade sensors, often expensive. To achieve accurate functionality in a constantly changing environment, this research proposes a ready-to-implement algorithm that integrates low-grade GPS and IMU modules uniquely by separately defining the weight of each GPS data entry to control its effect on Odometry. The weightage of each entry is determined based on collection factors such as the number of satellites used for GPS triangulation and is fused with IMU data sets to calibrate odometry and update path. This research study explores the proposed application of utilizing GPS and IMU for robot navigation between the Unitree GO1 robot dog and DJI Tello drone. A Socket Communication protocol is used to transmit positional and orientation data, between the UAV and the robot dog, to establish a navigation system that adjusts the UAV’s position for landing on the robot dog. It advances the integration of GPS and IMU technologies for precise navigation of mobile robots and UAVs, presenting a novel algorithm that effectively mitigates the challenges posed by environments with poor satellite connectivity and the potential for sophisticated and cost-effective navigational solutions in robotic applications.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Muhammad Hassan Tanveer
Enhanced Navigation Algorithm for UAVs and Mobile Robots using Low-Grade GPS and IMU Modules
Global Positioning Systems (GPS) have been widely used for mobile robot and unmanned aerial vehicle navigation for localization of the respective robot positions. This is often integrated with Inertial Measurement Units (IMU) for accurate positioning and odometry. A common navigation algorithm used to integrate these two sensors is using previously obtained velocity values and heading direction from IMU to calculate the current position, also known as dead reckoning, and fusing it with GPS data to calibrate the calculations and fix unexpected position errors. Such methods are simple to implement but are highly vulnerable to errors in environments with low satellite connectivity and thus, require high-grade sensors, often expensive. To achieve accurate functionality in a constantly changing environment, this research proposes a ready-to-implement algorithm that integrates low-grade GPS and IMU modules uniquely by separately defining the weight of each GPS data entry to control its effect on Odometry. The weightage of each entry is determined based on collection factors such as the number of satellites used for GPS triangulation and is fused with IMU data sets to calibrate odometry and update path. This research study explores the proposed application of utilizing GPS and IMU for robot navigation between the Unitree GO1 robot dog and DJI Tello drone. A Socket Communication protocol is used to transmit positional and orientation data, between the UAV and the robot dog, to establish a navigation system that adjusts the UAV’s position for landing on the robot dog. It advances the integration of GPS and IMU technologies for precise navigation of mobile robots and UAVs, presenting a novel algorithm that effectively mitigates the challenges posed by environments with poor satellite connectivity and the potential for sophisticated and cost-effective navigational solutions in robotic applications.