In this article, we propose a new method for dynamic obstacle avoidance in complex environments using unmanned aerial vehicles (UAVs). Our approach combines the strengths of two existing techniques: dynamic perception and state-of-the-art motion prediction. We improve upon these methods by incorporating real-time segmentation, allowing for more accurate object detection and tracking.
To understand how our method works, imagine you’re driving a car through a busy city intersection. You need to be able to quickly identify the movement of all the cars, pedestrians, and bicycles around you while also predicting where they will be in the future. Our proposed algorithm does something similar for UAVs, allowing them to avoid collisions with dynamic objects in their environment.
The key innovation of our method is the use of two special functions that help us segment the environment into smaller areas. This allows us to focus on specific parts of the environment at a time, making it easier to track and predict the motion of objects. We also use a new way of estimating the motion states of moving objects after segmentation, which gives us more accurate predictions and faster planning.
Our proposed algorithm is highly efficient and can be used in cluttered environments with many moving objects. We will demonstrate its effectiveness through comparisons with state-of-the-art methods in a later section. By improving upon existing techniques, our method has the potential to make UAVs even more useful for tasks like search and rescue, package delivery, and environmental monitoring.
Dynamic Obstacle Avoidance in Cluttered Environments: A Comprehensive Review
