G-1 (Medicine delivery system using hybdrid VTOL)

Summary:

Autonomous hybrid VTOL aircraft with folding wing configuration is methodical for the last-mile delivery of medicine for rural and remote areas. The designed aircraft can carry a payload of 4kgs with a flight time of up to 45mins and at a high stability speed of 65km/h. This drone is equipped with intelligent systems leading them to perform autonomously even in GPS (Global Positioning System) denied areas and away from obstacles helping for the disaster-prone areas. The folding wing configuration of this VTOL minimizes the Take-off and Landing area which is more reliable.

We use a simple methodology for connecting with the customer or reaching the public to the servitor. The end-users such as health centers/ emergency service centers request supplies through our mobile application. Along with the supply request, GPS coordinates of the customer are sent to the base station via server. The base station confirms the request and loads the UAV with the required accessories and sends it to the requested GPS coordinates. The UAV determines the shortest path and moves accordingly without getting in contact with any obstacles. Once the drone sets foot on the location it finds the feasible spot with the help of the arUco code landing pad. The entire procedure is supervised and monitored from the base station and can be overwritten manually if necessary. Fail-safe and Safety switches are enabled in the drones to make sure it is completely safe to use human interaction areas.

Medical services with drones for medicine deliveries and other emergencies like organ deliveries provide a better and quick response. With this, it is also suitable for delivery chains for faster deliveries within areas.

Aim of the Project:

India has a vast health care system, but there remain many differences in quality between urban and rural areas health care. Rural and remote areas are still facing a lack of medical emergency services. 27% of the total deaths in India happen with no medical attention at the time of death and 80% of those deaths happen in rural and remote areas according to a 2019 survey. The major reason for the deaths is the lack of delivery of drugs or emergency accessories on time.

During a pandemic or a natural calamity, the usual human-to-human contact delivery or door delivery will be affected, which impacts the supply chain. The necessary commodities including medicine will expire if they are not supplied in time and will be delayed. These difficulties create a new branch of a problem and increase exponentially until the necessary action is taken. In countries like India, these are major problems because the total population is around 137 crores.

Hence delivering food and medicine during pandemic situations, natural calamity, and medical emergency services in rural and remote areas using drone technology have created a greater impact in the medical field.

But the inefficiency of drones is another setback for the drone delivery system for its implementation. The orthodox way of drone delivery takes place with the use of a quadcopter or a multirotor, which is ineffectual due to its incapability of providing a moderate flight time, low cruise speed, etc.

To overcome these challenges we came up with an innovative solution of developing a hybrid VTOL. This VTOL is equipped with a folding mechanism to fold the wings, hence decreasing the surface area required to take off and land which is also capable of avoiding dynamic obstacles. The VTOL is proficient to navigate in GPS denied areas using Visual Inertial Odometry (VIO), thus feasible to deliver in hilly areas.

Objectives:

• Increase in flight time: The orthodox method used in drone delivery systems obeys conventional quadcopter or hexacopter drones to accomplish the missions. The major drawback of this system is failing to attain minimum flight time. Due to the synchronized power consumption by multiple motors throughout the mission, the flight time provided by the batteries will be very low. To overcome this issue, we are designing a VTOL (Vertical Takeoff and Landing) which is a combination of a plane and a multirotor. It adopts a multirotor configuration during the takeoff and landing, and a conventional plane configuration to complete the mission. Hence an increase in the flight time is achieved due to the usage of a single motor leading to a very less amount of power consumption from the battery.
• Folding mechanism of the wing: The conventional plane configuration is not adopted in UAV delivery systems due to its inefficient ways of takeoff and landing. This is due to the high surface area of the wing and the requirement of a long takeoff run for takeoff and landing. Hence we are designing a VTOL with a wing folding mechanism. which takes off and lands as a multirotor reducing the takeoff run. A unique wing folding mechanism is adopted in the aircraft during the takeoff and landings, hence making the UAV capable of working in compact places.
• Increase in cruise speed: The orthodox method used in drone delivery systems obeys conventional quadcopter or hexacopter drones to accomplish the missions. One of the major drawbacks of the system is low cruise speed. The maximum average speed of a multirotor can go up to 55kmph whereas a plane configuration has an average cruise speed of 80-110kmph. Hence this is one of the main reasons for adopting a plane configuration over a multirotor during the cruise mission. The wing of the aircraft is optimized and iterated numerous times with multiple airfoils to obtain an ideal wing that provides high lift, stability, and cruise speed.
• Landing: One of the main issues faced in the drone delivery system is landing, especially in rural and hilly areas. The conventional method adopted by the delivery companies is delivering by dropping from mid-air, due to the issues of landing. To overcome this unfit we have the solution of introducing arUco code landing pads in the landing zones. Once the aircraft reaches the desired GPS coordinate, it searches arUco landing pads with the help of its RGB camera and lands on it safely.
• GPS denied integration: In Hilly areas and dense, tunnel areas GPS is usually not reliable thus limiting the drones to only navigate in GPS areas. But these areas have an increasing demand for aerial works like delivery, rescue, etc. By using the Visual Inertial Odometry Technique, we can make drones navigate even in GPS denied areas. Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of a robot by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it. VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation.

Classification of the project:

Product/service development:

The current status of the product is under the prototyping and testing stage. The prototype has been tested and benchmarked in many hospitals to prove its redundancy and robustness.

Expected Outcome/Deliverables from the Project:

The medicine delivered to the remote and hilly areas is done using ground vehicles which are time-consuming and inefficacious. Presently, the drone delivery system adopts traditional multirotor which are unskilled and time-consuming due to its low cruise speed. To surmount this we ideated a design of developing a VTOL aircraft.

• The medicine delivered to the remote and hilly areas is done using ground vehicles which are time-consuming and inefficacious. Presently, the drone delivery system adopts traditional multirotor which are unskilled and time-consuming due to its low cruise speed. To surmount this we ideated a design of developing a VTOL aircraft.
• Since the conventional UAV delivery system vices numerous drawbacks such as low flight time, low cruise speed,takeoff and landing run etc. The adoption of the VTOL design is capable of tackling all the problems.
• Landing of the UAV during the delivery was one of the problems faced by the existing systems. To encounter this we ideated to introduce a landing pad enabled with arUco code in the landing zones. The UAV reaches the desired GPS coordinate and searches arUco landing pads with the help of its RGB camera and lands on it safely.
• To achieve maximum lift and stability, optimization of wing is done for numerous iterations with multiple airfoils.
  

Approach to the objectives:

Technology stack:

The above image shows the complete architecture of our system. The 8MMNavQ is used as a companion computer for the UAV to perform autonomous missions. Robot operating system (ROS) is implemented on the board. An intelligent flight management system is implemented for the integration of sensors to the UAV. The processor communicates with the flight controller through the MAVROS protocol. MAVROS is a ROS node that converts the ROS topic messages to MAVLink so that the communication between the onboard processor and the FMU is established. Image processing is done using the OpenCV library and implemented to the ROS ecosystem with the help of CV-bridge.

ArUco code detection landing is implemented for anaccurate and safe landing. A landing station is set up in the emergency centers and medical centers having an ArUco code. Once the drone reaches the destination it detects the code and lands on it. The communication between the UAV and the ground station is achieved by two methods.

Through RF signals: A long-range telemetry is used to retrieve the flight information of the drone via RF signals such as flight coordinates, altitude, cruise speed, etc.

Through 4G LTE Cellular BVLOS (Beyond Visual Line of Sight) Drone Control system is used for communication between the drone and base station. The advantage of the BVLOS system is that pilots can take control of the drone even though it's not inside the line of sight, live streaming is also achieved using 4G LTE.

Simulation:

Testing the algorithm for our drone is crucial. Thus testing it with real hardware is risky, because if there are any bugs or glitches in the code the drone might end up crashing thus damaging the components used. This can result in loss of time and money. Hence we decided to testbench all our code in the simulation

GAZEBO: We used the Gazebo to simulate the aircraft. Gazebo offers the ability to accurately and efficiently simulate populations of robots in complex indoor and outdoor environments.

https://drive.google.com/file/d/1W0-xUCeKvx4QOjndykbKf59mlMcg3Bzm/view?usp=sharing

https://drive.google.com/file/d/18LvvGleBHIXe4Xlx4jtqvE-Nkmkop-so/view?usp=sharing

PIX4 SITL:

Inorder to simulate the behaviour of drone precisely with real world, we utilized the PX4’s Software-In-The-Loop (SITL). WIth this we can interact with our drone just as you might with a real vehicle, using QGroundControl, an offboard API, or a radio controller. PX4 communicates with the simulator (e.g. Gazebo) to receive sensor data from the simulated world and send motor and actuator values. It communicates with the GCS and an Offboard API (e.g. ROS) to send telemetry from the simulated environment and receive commands.

MAVROS was used to convert the MAVLink messages from PX4 into ROS messages so that we can control the drone autonomously using ROS. In order to replicate the real-world environment, we used a particular world model in the gazebo along with the drone to make test benching the code more efficient.

URDF/SDF is used to model the drone which is simulated in the gazebo world. Plugins of sensors such as camera, imu, GPS, etc are used to get the sensor values from simulation. Camera feed from the sensor modeled in thegazebo was taken by subscribing to the camera topic and then it converted to OpenCV format using ROS-OpenCV Bridge. After converting to OpenCV format, the image is processed for ARUCO marker detection.

Obstacle Avoidance using real-time depth estimation:

Hilly areas in rural regions of India are widely populated with trees, rocks etc. Thus making it more challenging for drones to navigate to the delivery location. And also landing becomes difficult on uneven terrains.

We have used the 3D Vector Field Histogram for avoiding the obstacle using the point cloud obtained from the depth camera such as realsense. The obstacle avoidance will be running as a ROS node, which is subscribed to the depth-camera topic.

https://drive.google.com/file/d/1Myqa2aCFXptBsoP3bzK0aVc0WdcCWwc2/view?usp=sharing