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- W4379033023 abstract "The most complicated problems in the field of self-driving aircraft are the design of a strong real-time obstacle discovery and evasion framework. The issue is becoming a little complex due to the size and shape of the vehicle, and it becomes a little complex to accomplish the mission. As a result, we will be using a Flight Time Sensor (TOF sensor—time of flight) which is a lightweight sensor, and it is known as a MicroLiDAR sensor. The man’s behavior of detecting the collision status of the obstacles to the approach using the time-of-flight sensor is proposed here. The control board has related to a time-of-flight sensor and Artificial Intelligence (AI) in order to recognize the obstruction and prevent a collision. During the motion of the aerial vehicle (UAV), the detection set of rules estimates modifications inside the length of the upcoming obstruction zone. The strategy primarily recognizes the characteristic focuses of the deterrents and after that the impediments which are likely to approach the UAV. Another, by comparing the obstacle surface ratio, the UAV's position helps determine whether an obstacle can cause a collision. The algorithm was tested by doing actual flights, and the results show that it is accurate. With a focus on unmanned aerial vehicles, the study offers a thorough investigation of collision evasion methods for unmanned vehicles (UAVs); it could be a careful examination of a few collision avoidance strategies that are categorically characterized, besides a comparison of the ways taken into thought in connection to different circumstances and specialized contemplations. Also covered are how various sensor types are used in the context of UAVs to avoid collisions. It is highly accurate and collision avoidance ultimately happens." @default.
- W4379033023 created "2023-06-02" @default.
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- W4379033023 date "2023-01-01" @default.
- W4379033023 modified "2023-09-30" @default.
- W4379033023 title "Autonomous Drone Using Time-of-Flight Sensor for Collision Avoidance" @default.
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- W4379033023 doi "https://doi.org/10.1007/978-981-99-1767-9_5" @default.
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