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Sense and Avoid Technology for UAS
School of Engineering: Liberty University
AVIA 230: unmanned aerial systems
Dr. Jonathan Washburn
Jun 16, 2025
Introduction
Evolution of UAS Sense and Avoid Technology: As unmanned aerial vehicles (UAS) are
now available in a variety of applications, both commercial and governmental, challenges and
obstacles are presented in ensuring safe flight operations. These challenges include the ability of
UAS to avoid potential collisions. Resolving this problem is very important to increasing the use
and reliability of UAS. If we want to compare it to manned aircraft it’s going to be different
because it can avoid collisions visually without any technology, drones rely on very smart
technologies and sensors to avoid collisions and other potential hazards that go beyond human
capabilities (Yasin et al., 2020). Sense-and-avoid (SAA) technology is required to avoid these
obstacles, and its development has become very important nowadays for safety and to improve
UAS performance.
Initially, the SAA sense-and-avoid system existed, but it wasn't sophisticated. It used
basic sensors that could only detect close distances that were easy to detect, not long distances.
At the same time, there wasn't enough time to avoid obstacles and collisions. Those problems
made it difficult to trust and use UAS safely (Verfuss et al., 2019). Today, these systems have
become more sophisticated, with better accuracy. This is due to sensor types such as radar and
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lidar, that can detect and avoid any obstacles in difficult and diverse conditions. The use of
multiple sensors has shown a significant difference (Merei et al., 2025).
Why it is Important in UAS Operations: Not only is avoidance and sense technology a
technical achievement, it also represents a fundamental enabler for the wider use of UAS, for
example, navigating in different environments such as delivering packages, etc., which will
encounter obstacles and need to be avoided. To achieve this, algorithms that process sensor
data must be optimized in the shortest possible time (UNMANNED AIRCRAFT SYSTEMS beyond
VISUAL LINE of SIGHT AVIATION RULEMAKING COMMITTEE, 2022). A 2022 study
demonstrated that collisions are caused by delays. Even if this delay is only milliseconds or even
less, it is a major cause of these accidents. This study also demonstrated that UAS systems
exceeding 5 m/s require fast sensors to maintain safety. This study demonstrates that even if the
speed was low, it also requires an immediate response for safety (Ni et al., 2022). Strict
regulations have been imposed by regulatory bodies to enforce strict performance
requirements for collision avoidance systems, particularly for monitoring operations. These
regulations focus on the distance required to avoid a collision (Xu et al., 2020).
In this paper, we will examine the impact of SAA today and in the future, focusing on
practical applications and sensors. Research has proven that the development that has taken
place is indeed a major development. We see this development because it has been tested for
UAS, but it must be done in the face of major challenges in immediate processing and
environmental adaptation so that operations become safe and independent.
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Sensor Technologies
Passive Sensors: One of the most important systems in Sense and Avoid (SAA) is the
passive sensor, which detects electromagnetic radiation or acoustic signatures without
transmitting signals. The most common passive sensors are visual and optical systems. These
systems use smart cameras and algorithms to determine if there are any obstacles or a potential
collision. Passive sensors provide visual information and excel in low-light conditions, even in
difficult conditions. Visual information can be processed using computer vision to track multiple
objects simultaneously (He et al., 2023).
Infrared detection systems help passive sensors detect obstacles and collisions at night
or in the dark. This can be done by capturing the heat emitted by objects such as aircraft. These
systems are particularly useful for manned aircraft because they generate or track a large
amount of heat, but they are not particularly useful for drones because they generate less heat.
Acoustic sensors are a new technology used in UAS that detect obstacles through sound,
such as blade movements. This system works perfectly because it can hear from all directions,
even in difficult conditions like rain.
Active sensors: The second type of sensor is an active sensor, which sends signals and
analyzes echo signals to detect obstacles or collisions. Other uses include measuring distance
and speed, which is also important for avoiding difficult obstacles. Radars, such as FMCW, are
excellent at detecting in different weather conditions, and an FMCW system offers this solution
perfectly. It can also detect obstacles simultaneously.
Lidar technology is one of the most important technologies because it enables
threedimensional mapping using lasers, which helps determine with high accuracy whether
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there are any obstacles or collisions. Lidar technology is divided into two types: flash lidar and
scanning lidar. Flash lidar provides instant images of distances, while scanning lidar creates
detailed clouds of the surrounding environment. In other words, it contributes to building an
image containing details. These systems are very important and useful because they help
navigate tight spaces where the radar, we mentioned may not be sufficient.
Today, smaller radar and lidar systems have been developed and manufactured to fit the
size of a UAS, weighing less than 500g. Despite all these advances, lidar and radar technology
remain powerful and can detect any obstacle. Their new design makes them suitable for
different types of UAS.
Sensor Fusion: Sensor fusion is used to combine data from different sensors to provide a
clearer picture and overcome sensor limitations. Sensor fusion can also combine the strengths
of different sensors. For example, a radar system provides measurements to determine speed,
but it may not be able to classify objects. This is where optical systems come in to do this (Yeong
et al., 2025).
Machine learning helps with sensor fusion applications by learning strategies for
collecting data in different areas or environments. Neural networks simultaneously process
sensor data and detect complex patterns.
Data synchronization makes sure that sensor information is consistent in time and space.
Fusion of this data requires advanced computer techniques such as Kalman filtering and particle
filtering. These techniques help SAA operate reliably in different environments.
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Computational Methods and Algorithms
Obstacle Detection Algorithms: This technology helps to avoid and detect obstacles with
computer vision. This infrastructure uses edge detection and other techniques to identify
threats in camera images. Convolutional neural networks (CNNs) have played an important role
in detecting objects such as buildings, bridges, and birds, even in difficult conditions such as fog
(Vera-Yanez et al., 2023).
Machine learning does not only include computer vision; it also helps process sensors
such as radar, cameras, and lidar, which help avoid obstacles or dangerous collisions. These
technologies are evolving and improving every time they face difficult or dangerous conditions,
and the system becomes more reliable each time it is used and faces different obstacles.
The processing requires highly advanced algorithms that can be used on computing
resources with limited capabilities. Edge computing is very important because it allows us to
solve complex problems and obstacles while on board the aircraft. It is also important because it
helps solve problems on board the aircraft quickly.
Systems to Make Decisions: Threat assessment systems help UAS to detect any potential
collision threat and take fast decisions to avoid it. Factors such as speed, obstacle size, and type
are considered, as well as environmental conditions. Decision analysis techniques help ensure
that appropriate and safe decisions are made.
Path planning technology helps UAS adjust and fix their trajectory in real time to safely
avoid obstacles and collisions. This type of technology uses advanced techniques such as rapidly
exploring random trees (RRT), which helps find a safe path in complex environments. This
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system also avoids any obstacles, not just stationary ones, which helps UAS navigate safe paths
and consider changes in the operational environment (Hu et al., 2025).
Distributed decision-making systems enable coordinated responses with many aircraft
simultaneously in the same airspace. These systems use cooperative game theory to avoid
collisions and ensure that the drone's movement does not pose a threat to other aircraft. A
2021 study demonstrated that these systems would function well even if one of the aircraft
experiences a problem and loses contact (YU et al., 2021).
Conclusion
Technical Challenges: Even though SAA technology has made significant improvement in
the UAS world, there are still challenges and limitations facing the full autonomy of UAS. These
challenges include size and power associated with many UAS platforms, which limit the
deployment of advanced sensor suites. Sensor technologies often struggle to adapt to the area.
Processing delays are a major issue and become more significant as UAS operating
speeds increase. It takes time to gather the required data, assess and address threats, and, most
importantly, implement the correct decision. This creates challenges and delays, as these are
the factors that determine safe distances.
Providing clear rules for SAA remains a constant challenge. Aviation authorities are
providing standards and regulations, but the problem is that there is still no global agreement,
making it difficult to achieve consistency across different UAS types.
Future Research: Research focuses on fixing any technical limitations that could limit the
capabilities of SAAs while at the same time supporting difficult scenarios. A promise from
Artificial intelligence (AI) is that systems and technologies can overcome any obstacles and
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improve upon any failures or problems over time. According to researchers, these systems will
develop to the point where they will significantly outperform human capabilities and be able to
avoid collisions without human intervention.
Cooperative sensing and distributed intelligence enable UAS to work together to bypass
individual platforms. Sharing sensor data and computing power helps UAS better understand
their surroundings. This collaborative work will let the UASs overcome any obstacles and allow
them to operate more safely in challenging and complex areas.
The future of UAS depends on how SAA technology advances and how it can safely avoid any
potential obstacles. As this system improves, opportunities and the results of improvement will
show in different areas, such as environmental monitoring and connectivity. One of the most
important and difficult challenges facing UAS is building secure SAA systems, especially given
the presence of stringent regulations.
In my opinion, future research should focus on developing protocols to accurately
evaluate SAA systems across different situations. Bio-inspired sensing methods are new ideas
that can improve UAS collision avoidance. What makes UAS exciting is the integration of shared
sensor networks with artificial intelligence.
Isaiah 30:21 says: Whether you turn to the right or to the left, your ears will hear a voice
behind you, saying, “This is the way; walk in it.”. I likened this verse to the topic I talked about
because this verse talks about the presence of awareness and this is due to God’s guidance and
this is like the SAA systems, directional guidance to avoid collision and obstacles.
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References
He, D., Cui, Y., Ming, F., & Wu, W. (2023). Advancements in Passive Wireless Sensors,
Materials, Devices, and Applications. Sensors, 23(19), 8200–8200.
https://doi.org/10.3390/s23198200
Hu, W., Chen, S., Liu, Z., Luo, X., & Xu, J. (2025). HA-RRT: A heuristic and adaptive RRT algorithm
for ship path planning. Ocean Engineering, 316, 119906.
https://doi.org/10.1016/j.oceaneng.2024.119906
Merei, A., Hamid Mcheick, Ghaddar, A., & Djamal Rebaine. (2025). A Survey on Obstacle
Detection and Avoidance Methods for UAVs. Drones, 9(3), 203–203.
https://doi.org/10.3390/drones9030203
Ni, S., Wang, N., Li, W., Liu, Z., Liu, S., Fang, S., & Zhang, T. (2022). A deterministic collision
avoidance decision-making system for multi-MASS encounter situation. Ocean
Engineering, 266, 113087. https://doi.org/10.1016/j.oceaneng.2022.113087
UNMANNED AIRCRAFT SYSTEMS BEYOND VISUAL LINE OF SIGHT AVIATION
RULEMAKING COMMITTEE. (2022).
https://www.faa.gov/regulations_policies/rulemaking/committees/documents/media/
UAS_BVLOS_ARC_FINAL_REPORT_03102022.pdf
Verfuss, U. K., Aniceto, A. S., Harris, D. V., Gillespie, D., Fielding, S., Jiménez, G., Johnston, P.,
Sinclair, R. R., Sivertsen, A., Solbø, S. A., Storvold, R., Biuw, M., & Wyatt, R. (2019). A
review of unmanned vehicles for the detection and monitoring of marine fauna. Marine
Pollution Bulletin, 140, 17–29. https://doi.org/10.1016/j.marpolbul.2019.01.009
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Vera-Yanez, D., Pereira, A., Rodrigues, N. F., Pascual, J., García, A. S., & Fernández-Caballero, A.
(2023). Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A
Systematic Review. Journal of Imaging, 9(10), 194–194.
https://doi.org/10.3390/jimaging9100194
Xu, C., Liao, X., Tan, J., Ye, H., & Lu, H. (2020). Recent Research Progress of Unmanned Aerial
Vehicle Regulation Policies and Technologies in Urban Low Altitude. IEEE Access, 8,
74175–74194. https://doi.org/10.1109/access.2020.2987622
Yasin, J. N., Mohamed, S. A. S., Haghbayan, M.-H., Heikkonen, J., Tenhunen, H., & Plosila, J.
(2020). Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and
Approaches. IEEE Access, 8, 105139–105155.
https://doi.org/10.1109/access.2020.3000064
Yeong, D. J., Panduru, K., & Walsh, J. (2025). Exploring the Unseen: A Survey of Multi-Sensor
Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles. Sensors, 25(3),
856. https://doi.org/10.3390/s25030856
YU, Z., ZHANG, Y., JIANG, B., FU, J., & JIN, Y. (2021). A review on fault-tolerant cooperative
control of multiple unmanned aerial vehicles. Chinese Journal of Aeronautics.
https://doi.org/10.1016/j.cja.2021.04.022
