Design Summary & Analysis (Draft 3)

How Flying Robots Might Prevent Illegal Deforestation

 

MEC1281

 

Design Summary & Analysis

 

Draft 3

 

By Thong Fu Lin

 

21 February 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 drones acted as "tiny, silent guardians of the rainforest" and gathered data from disaster zones and for 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) stated that the quadrotors operate automatically for spying, unlike the "fixed-wing drone" that pilots manually. 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; 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 determined its position using a laser beam by sensing the distance away from an object. With its coordinate system, the quadrotor can navigate without a GPS. According to the article "Attractive Ellipsoid-Based …" (Falcón et al., 2020, p. 7851-7860), the QBall2 by Quanser 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. Thus, the QBall2 infrared camera would be more superior in terms of tracing capabilities.

 

Another concern of the quadrotor is its spatial awareness. It allows the quadrotor to react quickly to obstacles and change its trajectory. The four rotors rotate independently and at different speeds to pivot its body forward or swivels. The onboard processor sends signals to the rotors 600 times per second, which allows it to respond quickly. According to an article “Quadrotor trajectory tracking and …” (Cai et al., 2019, p. 636-654), the chaotic grey wolf optimization (CGWO)-based active disturbance rejection control (ADRC) is a control scheme that improved tracking performance in the presence of external disturbance. With CGWO-based ADRC incorporated with the quadrotors, it will resolve the trajectory tracking and obstacle avoidance issue. Hence, with the improving functions, the quadrotor can maneuver in confined spaces and avoid obstacles easier.

 

Lastly, 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 drones to have a hard time working together as a team. During his TED talk, Kumar demonstrated the quadrotors coordinating their movement by sensing the surrounding neighbors. According to the article “An Introduction to Formation …” (Yu et al., 2019, p. 181-191), the UAVs coordinated with each other while maintaining equal distance. Thus, by having the quadrotors maintain their distance and sense each other, the quadrotors can effectively prevent the lack of communication from hindering its teamwork.

 

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

 

 

References

 

Peck, M. (2012, March 20). How flying robots might prevent deforestation. Retrieved February 06, 2021, from https://mashable.com/2012/03/20/flying-robots-deforestation/

 

Kumar, V. (n.d.). Transcript of "robots that fly ... and cooperate". Retrieved February 12, 2021, from https://www.ted.com/talks/vijay_kumar_robots_that_fly_and_cooperate/transcript

 

R. Falcón, H. Ríos, M. Mera and A. Dzul, "Attractive Ellipsoid-Based Robust Control for Quadrotor Tracking," in IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7851-7860, Sept. 2020, doi: 10.1109/TIE.2019.2942546.

 

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

 

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

 

Yu Y., Liu Z., Wang X. (2020) An Introduction to Formation Control of UAV with Vicon System. In: Lu H., Yujie L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_17