Enhancing Quadcopter Navigation with Energy-Efficient Obstacle Avoidance Using the KNN Algorithm and Distance Deviation

One particular kind of UAV that is frequently employed for searching and rescue, reconnaissance, and other purposes is the quadcopter. To reduce the amount of human oversight during quadcopter navigation, an obstacle avoidance system is required. With obstacle dimension information, obstacle avoidance systems can be designed to select avoidance paths. Energy efficiency must be taken into account while determining the avoidance direction of quadcopter flights due to power limits. The obstacle avoidance system in this study assesses an object’s dimensions, energy consumption, and distance from the quadcopter before determining which direction to avoid it-upward, leftward, or rightward. The quadcopter navigates in a three-dimensional environment. With 98.7% accuracy and 0.0062 s of computing time, KNN (K-Nearest Neighbor) generates effective avoidance judgments. According to the simulation, the quadcopter may arrive at the desired location without running into any stationary obstructions. When impediments are detected, the quadcopter can also select the most effective avoidance direction.