Design Summary & Analysis (Draft 1)

The article "The Flying Robot Might Prevent Deforestation" (2012) introduces the functionality and purpose of drones, which provide aerial surveillance and data gathering. The drones act as "tiny, silent guardians of the rainforest" and gather data from disaster zones and for illegal logging. Robots will be able to capture live footage and allows immediate response to the situation. The article also states that drones are 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), quadrotors can 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 rotors' speed. However, each quadrotor is independent, which will affect the coordination with other units. While the quadrotor remains a solution to reduce the damage to the ecosystem, operations might compromise as communication among each quadrotor remains a concern.

The primary aspect of the quadrotor is its tracing capabilities. In the TED talk, Kumar states that the quadrotor can function using a camera and laser. It can map out the environment and determine 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 …"(2020), the QBall2 by Quanser uses “six synchronized infrared cameras” to track its position and attitude. The infrared camera captures radiation energy and generates an image of an object's infrared radiation. Thus, the QBall2 infrared camera would be more superior in terms of tracing capabilities.

The secondary aspect 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 …” (2019), the chaotic grey wolf optimization (CGWO)-based active disturbance rejection control (ADRC) control scheme improves tracking performance in the presence of external disturbance. Hence, with improving functions, the quadrotor can maneuver in confined spaces and avoid obstacles easier.

The final aspect 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 will affect the drones to have a hard time working together as a team. During his TED talk, Kumar demonstrated the quadrotors coordinate their movement by sensing the surrounding neighbors. According to the article “An Introduction to Formation …” (2020), the UAVs coordinate 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 aspects to the quadrotor that requires attention. The functionalities with regards to communication, tracing capabilities, and spatial awareness, can be improved.

 

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