UAV Obstacle Avoidance 101
When you are a UAV operator, the chances are that you have come across the phrase "UAV obstacle avoidance." This article will give you an overview of what this term means and how to implement it in your system. You will learn to avoid objects, detect pedestrians, and use sensors to track and follow objects. It is an excellent resource for anyone interested in UAVs.
Object descriptors for UAV obstacle avoidance are a significant problem that affects many autonomous systems. This task aims at the highest accuracy while remaining within the computational budget.
There are many approaches to building 3D models of obstacles in the environment. One of these uses a polynomial regression to fit sensor measurements. Another system uses a stereo camera to calculate the distance between obstacles.
This approach is a novelty. In addition to estimating the object's size, the algorithm calculates its position and motion.
The algorithms used in this research were evaluated in a variety of tests. Some actual flight experiments were conducted in both indoor and outdoor environments. These tests proved the performance and reliability of these algorithms.
This research also demonstrated a UAV's capabilities to perform various avoidance maneuvers. It is not surprising that an intelligent autonomous quadrotor can be helpful in different scenarios.
For instance, it can perform a hover stability flight in which it flies in a direction opposite to that of an approaching obstacle. It can also detect frontal obstacles using a monocular camera.
It also shows that the IPOPT algorithm can be considered a real-time obstacle avoidance algorithm. This is thanks to its ability to recalculate the path of the UAV after detecting an incoming obstacle.
The algorithms have been tested on a hundred actual flight experiments with 1000 obstacles. This is a good indication of the trustworthiness of the systems.
It also demonstrates that the largest obstacle may not be the best indicator of its size. This is because visual conditions can play a role in the accuracy of obstacle detection.
An obstacle avoidance module is one of the most important modules of autonomous navigation. It incorporates a collision prediction network to help reduce the risk of collisions between UAVs. Several approaches have been developed to identify obstacles. These include SLAM-based methods, optical flow-based methods, and lane detection methods.
SLAM-based methods are proper because they generate precise metric maps of the environment. However, they suffer from some common problems. These include computational complexity, sensitivity to illumination changes, and limited accuracy.
Obstacle avoidance is a crucial step in designing and developing autonomous systems. Identifying and avoiding potential hazards, it can significantly increase the autonomy of UAVs. Besides, it can significantly improve pilot operation errors.
Various obstacle avoidance techniques have been developed in recent years. These include SLAM-based methods, edge shifting, and optical flow-based methods. These techniques are all designed to detect obstacles and determine their distances.
SLAM-based methods have been used to develop a mapping system that tracks a UAV's position and orientation. This mapping system is becoming increasingly popular due to the rapid development of visual SLAM techniques.
A stereo-based method produces a real-time 3D environment map using two or more synchronized cameras. The camera must be properly calibrated. Similarly, sequential Monte Carlo techniques or extended Kalman filters may perform data fusion from multiple sensors.
Edge shifting approach is an obstacle-avoidance technique that has been applied to mobile robots. It detects large objects. This approach can be used to avoid obstacles on roads.
Unlike lane detection, this approach does not apply to unknown environments. This method is not suitable for use in cars.
Optical flow variations
Optical flow variations for UAV obstacle avoidance can be accomplished using various methods. These techniques vary depending on the environment. They can be phase-based, energy-based, or gradient-based. The algorithm we propose incorporates the three. It is tested on real and virtual environments. The method was validated with indoor flight experiments and simulations.
The best-performing mask has a vector size of 15 by 20. It is essential to select a good texture to avoid pixelation. In addition, the lens should not be too discontinuous.
An illustrative simulation was performed to assess the effect of yaw rates on optic flow. A comparison was made between the effects of a yaw-rate compensation and an EOF compensator. The yaw-rate compensation reduces the effects of a significant yaw rate.
A small remote racing car was used for the experiments. The vehicle was rear-wheel driven and based on an Ackerman steering configuration. The camera was a stereo camera. The distance sensor could be employed to estimate the velocity.
The method we propose incorporates a waypoint guidance strategy, a proportional-derivative (PD) controller, and a yaw-rate effect compensator. The approach uses a simple algorithm that avoids obstacles in 3-D environments by combining waypoint guidance and yaw-rate compensation. It also includes guidance to the desired position.
The approach was implemented on a customized MAV. It was impossible to conduct a live video stream because of time and budget constraints. However, a few videos were captured to test the system.
Optical flow variations for UAV obstacle avoidance can be a tricky subject. This article focuses on the most effective and innovative ways to use the technique.
Track and follow objects sensors
The track and follow objects sensors can detect people and vehicles moving along a drone's path. These sensors also help drones avoid obstacles in an indoor environment.
One of the biggest challenges to flying inside a warehouse is avoiding collision with other objects. The obstacle detection process is a complex task and must be accurate and fast. The method includes many complex technologies. However, it is possible to build an autonomous UAV that can fly indoors safely without being tripped up.
A vision-based dynamic obstacle avoidance system is the most advanced approach to this problem. It is based on three main components: a lens, a sensor, and a light source. In addition, it requires high computation.
In addition to cameras, the system uses a time-of-flight sensor. These are highly accurate and provide the scene's intensity in real-time. The sensor also captures depth information at the same time.
The algorithm translates visual data from the camera into actionable information. The proposed method has been tested on open video sequences and self-captured simulated data. It was found that the technique can detect two moving objects faster than existing SoA algorithms.
Using a stereo vision sensor, the system detects obstacles and provides relative depth. However, the depth estimation of RGB-D sensors is noisy.
To further improve the accuracy of the obstacle dimension estimation, the authors implemented dynamic binary thresholding. This method significantly improved the accuracy of obstacle detection.
In addition, the system used a multivariate Gaussian probability density function. This method uses Kalman filtering to predict the position of the obstacles.
A u-depth map is another tool that can be used for obstacle detection. It is calculated by determining the depth bins in the vicinity of an obstacle. These bins are kept in a growing order from top to bottom.