Draft Summary and Analysis (Draft 4)

How Flying Robots Might Prevent Illegal Deforestation

 

MEC1281

 

Design Summary & Analysis

 

Draft 4

 

By Thong Fu Lin

 

2 April 2021

 

The article "The Flying Robot Might Prevent Deforestation" (Peck, 2012) introduced the functionality and purpose of drones which provided aerial surveillance and data gathering. The quadrotors acted as "tiny, silent guardians of the rainforest" and gathered data from disaster zones and on illegal logging. Robots can capture live footage and allow immediate response to the situation. Mario Campos, a professor from the Federal University of Minas Gerais stated that drones were used in recent years to "detect illegal drug trafficking and mining, as well as environmental crimes" by keeping tabs in the sky. According to the University of Pennsylvania deputy dean Kumar, one of the co-founders of the quadrotor, the drones operate automatically for spying, unlike the "fixed-wing drone" that is piloted manually. He also stated that the quadrotor is smarter and has high situation awareness to react to obstructions by orientating themselves and maneuvering through by adjusting the rotor speed. However, each quadrotor is independent, which affects the coordination with other units. Kumar described the quadrotor as a solution to reduce the damage to the rainforest eco-system but the functionalities such as tracing capabilities, spatial awareness, and communication can affect its operation for surveillance and data gathering on illegal logging.

 

One of the concerns for the quadrotor is its tracing capabilities. In a TED talk (2021), Kumar stated that the quadrotor functioned using a camera and laser. It can map out the environment and determine its position using a laser beam for sensing the distance away from an object. With its coordinate system, the quadrotor can navigate without a GPS that limits its navigation system that is solely dependent on satellite signal transmission. According to Falcón et al. (2020), the QBall2 by Quanser, in contrast, used “six synchronized infrared cameras” to track its position and attitude. The infrared camera captures the radiation energy of an object and generates an image with infrared radiation. It has frame rates up to 360 Hz with submillimeter accuracy that can resolve the three-dimensional location of the drone’s rigid body. Thus, making the QBall2 infrared camera by Quanser more superior to the quadrotor in terms of tracing capabilities with its high accuracy detection.

 

Another concern of the quadrotor is its spatial awareness, the drones react quickly to obstacles and change their trajectory. The four rotors rotate independently and at different speeds to pivot its body forward or swivel. The onboard processor sends signals to the rotors 600 times per second, which allows it to respond quickly. However, the drone is unable to mitigate external factors such as surroundings and wind that would affect its overall flight performance. According to Cai et al. (2019), the chaotic grey wolf optimization (CGWO)-based active disturbance rejection control (ADRC) is a control scheme that improves tracking performance in the presence of external disturbance. CGWO-based ADRC could be incorporate into quadrotors to resolve trajectory tracking, obstacle avoidance, and flight. With improve features and functions, the quadrotor could maneuver in confined spaces and avoid obstacles easily.

 

The final concern of the quadrotors is the communication with each unit. Kumar stated that the quadrotor does not have a connection between each unit. The lack of communication affects the drone such that the rotors have a hard time working together as a team. The drones may collide with each other as a result while completing a task together. According to Yu et al. (2019), the Unmanned Aerial Vehicles (UAV) coordinates with each other while maintaining equal distance during movement. During his TED talk, Kumar demonstrated the quadrotors coordinating their movement by sensing the surrounding neighbors. By improving the quadrotors to sense and maintain distance would allow the drones to achieve coordinated movement and preventing them from colliding. Thus, having the quadrotors maintain their distance and sense each other could effectively prevent the lack of communication from hindering their teamwork and improve work efficiency.

 

In conclusion, the quadrotor does the job by surveilling the forest in the sky. However, there are concerns about the quadrotor that require attention for further improvement. the suggested changes would allow the quadrotor to perform at a better rate than its predecessors. 

 

 

References

Cai, Z., Lou, J., Zhao, J., Wu, K., Liu, N., & Wang, Y. (2019). Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control. Mechanical Systems and Signal Processing, 128, 636–654. https://doi.org/10.1016/j.ymssp.2019.03.035 

Chui, A. C., Gittelson, A., Sebastian, E., Stamler, N., & Gaffin, S. R. (2018). Urban heat islands and cooler infrastructure – Measuring near-surface temperatures with hand-held infrared cameras. Urban Climate, 24, 51–62. https://doi.org/10.1016/j.uclim.2017.12.009

Kumar, V. (n.d.). Transcript of "robots that fly ... and cooperate". Ted.com.  https://www.ted.com/talks/vijay_kumar_robots_that_fly_and_cooperate/transcript

Peck, M. (2012, March 20). How flying robots might prevent deforestation. Mashable. https://mashable.com/2012/03/20/flying-robots-deforestation/

 

Falcón, R., Ríos, H., Mera, M., and Dzul, A. (2019) Attractive ellipsoid-based robust control for quadrotor tracking. IEEE Transactions on Industrial Electronics, 67(9), 7851-7860,

https://doi.org/10.1109/TIE.2019.2942546

 

Yu, Y., Liu, Z., & Wang, X. (2019). An introduction to formation control of UAV with Vicon System. 2nd EAI International Conference on Robotic Sensor Networks, 181–190. https://doi.org/10.1007/978-3-030-17763-8_17

Edited on 8th April 2020