Insight into Autonomous Navigation of Micro Aerial Vehicle (MAV)

Review Article

Austin J Robot & Autom. 2014;1(1): 3.

Insight into Autonomous Navigation of Micro Aerial Vehicle (MAV)

Subramani T1*, Saranya M1 and Neelaveni R2

1Department of Robotics and Automation Engineering, PSG College of Technology, India

2Department of Electrical and Electronics Engineering, PSG College of Technology, India

*Corresponding author: Subramani T, Department of Robotics and Automation, PSG College of technology, Peelamedu, Coimbatore – 641004, India

Received: October 30, 2014; Accepted: November 28, 2014; Published: December 02, 2014


Researchers focus mainly on autonomous navigation of Micro Aerial Vehicle (MAVs) in GPS-denied environments. This is because MAVs offer major advantages when used for aerial surveillance, reconnaissance, and inspection in complex and dangerous environments. Indeed, they are better suited for dull, dirty, or dangerous missions than manned aircraft. MAV is a class of Unmanned Aerial Vehicle (UAVs). Further, many technological, economic, and political factors have encouraged the development and operation of Unmanned Aerial Vehicle (UAVs) and its class of MAVs. This review provides knowledge about different methodologies available to confer autonomy to MAV.

Keywords: MAV: Autonomous Navigation; GPS-denied; Optical Flow Algorithm


One of the key research fields over the last few decades is the autonomous vehicles. Based on the working environment, a rough classification of the autonomous vehicles would include Unmanned Aerial Vehicles (UAVs), Un-manned Ground Vehicles (UGVs), Autonomous Underwater Vehicles (AUVs), and Autonomous Surface Vehicles (ASVs). The autonomous unmanned aircraft equipped with autonomous control devices is called Unmanned Aerial Vehicles (UAVs). A Micro Aerial Vehicle (MAV) is a class of UAVs with respect to its size. In recent years, there is a rapid growth development in MAVs. Development is driven by commercial, research, government, and military purposes.

The importance of MAV has grown now as it is very efficient. The MAV are currently used to do home delivery of products, it is used for surveillance in military, stone quarries for inspection and also in application where human cannot enter. These applications require the MAV with good navigation skills. The good navigation skills will help to complete the work in timely manner.

This review aims to give the readers an insight into the autonomous navigation of MAV. Review discusses various autonomous control devices used by the researchers to develop an autonomous MAV. Conferring autonomy upon MAVs, so they can detect and navigate through voids and locate and land on a surface is a highly essential and challenging task. The degree of autonomy is measured by the ability to navigate through cluttered environments, maneuver close to obstructions, avoid obstacles, and take off and land. And also it includes navigation inside caves and dense forests where GPS will not work. This makes the selection of algorithm very challenging. The reason is that the multiple data is to be fused to arrive a solution. Payload restrictions have often constrained the autonomy capabilities. Another restriction includes choosing type of MAV whether it is having quad rotor or hexa rotor. For the purpose of autonomy in MAV motion, the camera sensor is used. Hence Vision based autonomous navigation of MAVs is the keen topic discussed in this review.

Autonomous MAV

Some of the different methodologies that confer autonomy to MAV are as follows.

Monocular camera with classical pid controller

This methodology is proposed by Yingcai Bi and Haibin Duan of the Beijing University, China [1]. They propose a hybrid system consisting of a low-cost quad rotor and a small pushcart. The autonomous navigation of quad rotor is made with the help of PID controller. MAV is controlled with classical Proportional–Integral– Derivative (PID) controller for autonomous visual tracking and landing on the moving helipad carrier. The vision-based tracking and landing approach utilizes RGB color information rather than grayscale information of the helipad. Thereby autonomous MAV shows fast and robust performance in different lighting conditions. The model utilizes the off-the-shelf affordable quadrotor thereby the complex task is performed using the relative pixel position information in image plane without communication between the quadrotor and carrier. The quadrotor’s relative position to helipad is estimated with a frequency up to 30 Hz from the video stream, which enables the quadrotor to fly autonomously while performing real-time visual tracking and landing on the carrier. Figure 1 depicts the steps involved in the process of tracking and landing of MAV using PID controller and Figure 2 gives the block representation.