The document discusses the development of sonar-based obstacle avoidance for unmanned aerial vehicles (UAVs), highlighting its importance for military and humanitarian applications. It outlines the project's objectives, including the creation of a quadrotor UAV capable of obstacle avoidance, and describes the communication and control systems utilizing the Mavlink protocol. Recommendations for future work include exploring stereo vision for object detection and enhancing 3D obstacle avoidance capabilities.

1. SONAR Based Obstacle
Avoidance for UAVs
Submitted By
Arti Kalra (SID: 13101021)
Prashant Sharma (SID: 13101017)
Gaurav Dhir (SID: 13101013)
Gunjit Dhingra (SID: 13101007)

2. Research Motivation
1. Research pertaining to Unmanned Aerial Vehicles (UAVs) and especially
obstacle avoidance continues to increase due to the versatility of their
potential use for both military and humanitarian missions such as search and
rescue, surveillance of disaster stricken areas, and battlefield assessment.
2. Potential military application includes the use of such vehicles for
reconnaissance inside unfamiliar buildings for situational awareness before
sending troops in. In a similar scenario, vehicles with obstacle avoidance
could allow for search and rescue teams to find survivors in buildings
damaged by a disaster without the risk of injuring the rescuers.

3. Major Objectives of the Project
1. To successfully develop quadrotor UAV capable of performing obstacle
avoidance using SONAR functionality.
2. To test and develop interface for communication between RPI and Pixhawk
3. To develop code for transferring offboard control from Pixhawk to RPI and
allowing RPI to perform control maneuvers in GPS denied environment

4. Popular Approaches towards performing Obstacle
Avoidance
1. Stereo Vision Based Obstacle Avoidance Systems
2. Laser Based Obstacle Avoidance Systems
3. SONAR Based Obstacle Avoidance Systems
4. Optical Flow Based Obstacle Avoidance Systems

5. Major Limitation of Vision Based Approach
1. High Latency
2. Object Detection remains one of the challenging aspects which is still to be
sorted out
3. Requires usage of complicated machine learning and neural network
algorithms and hence requires greater time investment
4. Hardware not currently available in the country.

6. Obstacle Avoidance System (Closed Loop System)
SONAR
Senses
Obstacle
Information is
forwarded to
Pixhawk
Pixhawk interacts
with RPI via
MAVLINK
RPI provides offboard
control and takes
necessary maneuver
action
Offboard Control is
Disabled and control is
transferred to Pixhawk

7. Major Components of the UAV
Hardware:
1. SONAR System
2. Inertial Measurement Unit
3. Pixhawk Flight Control System
Software:
1. Onboard Communication via MAVLINK Interface from RPI to Pixhawk
2. SONAR Data Retrieval System via MAVLINK
3. Offboard Control via RPI through Navigation Commands via MAVLINK

8. Interfacing RPI3 with Pixhawk
Communication of Raspberry Pi and Pixhawk using a MAVLINK protocol over a
serial connection.

9. Interfacing Pixhawk with Analog SONAR

10. MAVLINK Interface with Pixhawk
1. A code is developed to provide serial communication between RPI and
Pixhawk
2. This remains the major contribution of our project and this code can be
utilized further for research and development purposes.
3. The code basically is based on developing utilizing common MAVLINK
headers to retrieve information from pixhawk
4. The code also provides support for path planning algorithms and additional
sensor data to provide complete offboard control of the UAV
5. The code will also be uploaded on GITHUB as an open-source contribution.

11. MAVLINK Messages
1. MAVLink is a very lightweight, header-only message marshalling library for
micro air vehicles.
2. It can pack C-structs over serial channels with high efficiency and send these
packets to the ground control station
3. MAVLink messages are defined in XML and then converted to C/C++, C# or
Python code (several generators exist).

12. MAVLINK Common Message Set Specification

13. Future Scope and Recommendations
1. Object Detection using stereo vision needs further investigation with the
concept relying on neural network and machine learning algorithms for robust
performance.
2. The autopilot interface developed for communication between RPI and
PIXHAWK can be utilized as a starting point for developing various path
planning algorithms.
3. One popular method involves the utilization of occupancy grid algorithm using
stereo vision and Markov Decision Processes for performing optimal path
planning

14. Future Scope and Recommendations
1. Our concept only provides one dimensional object avoidance using a single
SONAR sensor
2. Several SONAR sensors can be incorporated at different angles to provide 3
Dimensional Object avoidance.
3. However, 3 Dimensional obstacle avoidance using SONAR has some
limitations due to difficulty in accurate data processing arising due to
interference between various SONAR sensors