Development of Obstacle Detection System Based On the Integration of Different Based Sensor for Small-Sized UAV

Due to the physical size and weight limits of small unmanned aerial vehicles (UAVs), developing a reliable obstacle detection a system that can provide an effective and safe avoidance path is extremely difficult. Prior work has tended to use a vision-based sensor as the primary detecting sensor however, this has resulted in a high reliance on texture appearance and a lack of distance sensing capabilities. Furthermore, due to the inability to detect the free region, vision-based sensor detection systems have difficulty developing a trusted safe avoidance path.  However, most wide spectrum range sensors are bulky and expensive, making them unsuitable for small UAVs. This project aims to construct an obstacles detection system with the integration of various based sensors for a small UAV. The potential obstacles are identified by categorizing feature points identified in image frame. The suggested approach was tested in a real-world setting for both of the observed scenarios, which included various obstacles configurations. Two types of scenarios are experimented in this project consists of single frontal obstacles and presence of side obstacles alongside the frontal obstacles. On top of that, the position of the side obstacle is aligned to the frontal obstacle and then will be positioned in the increment of 20cm further from the frontal obstacle in order to analyse the outcome of the proposed algorithm. The proposed detection system had a possibility to be a trustworthy system even after utilising the depth perception technique, however this does not imply that the proposed system is faultless. The results show that the suggested algorithm system detects and distinguishes between the potential obstacles and free region for a single frontal obstacle perfectly. However, there were improvements that should be implemented with the proposed system's ability of detection for multiple obstacles

Mechanism System to Reduce the Pendulum Effect of a Load for Delivery Drone

Unmanned aerial vehicles (UAVs), also referred to as "drones," are becoming more widely accepted as a typical form of transportation for a range of purposes in the logistics sector. Drones are anticipated to outperform traditional vehicles in a number of ways, including with a constant, quick speed, travel is straightforward, there is no exposure to traffic, and there is no requirement for physical transportation infrastructure. When an aircraft is in a sideslip, these surfaces generate sideward lift forces, which is known as the pendulum effect. The keel effect, also known as the "Pendulum Effect," occurs when the effect of sideways forces above the center of gravity in producing a rolling moment is increased by having a lower center of gravity. The load shift, also known as the pendulum effect, reduces flight efficiency and may unbalance the UAV, resulting in catastrophic failure. The purpose of this project is to construct a mechanism system to reduce the pendulum effect of a load for the delivery drone and to investigate the numbers of pendulum sway with the different types of loads. The mechanism for this project was constructed by using a 3D printer. The experiment process was used to carry out this project. This project's variables included various types of loads, string length, and the number of sways. The data obtained in this project are important to determine which design is the best for the delivery drone. Unfortunately, the data from the experiment gave the same result for all designs. For future studies try to come up with new concepts for how to construct the mechanism system to reduce the pendulum effect of a load for delivery drone and compare all the concepts

Obstacle Detection for Unmanned Aerial Vehicle (UAV)

This study aims to develop an obstacle detection system for unmanned aerial vehicles utilising the ORB feature extraction. In the past, small unmanned aerial vehicles (UAV) were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses different advantages and disadvantages. As a result, the small unmanned aerial vehicle is unable to determine the obstacle's distance or bearing precisely. Due to physical size restrictions and payload capacity, the lightweight Pi Camera and TF Luna LiDAR sensor were selected as the most suitable sensors for integration. In algorithm development and filtration is used to improve the accuracy of the feature matching process, which is required for classifying the obstacle region and free region of any texture obstacle. The experiment was under the environment of OpenCV and Spyder. In real-time experiment, the success rate for good texture(40%), poor texture (55%) and texture-less (45%) The findings indicate that the recommended method works well for detecting textures-less obstacle even though the success rate is only 40% because out of 10 test only one test is fail on detecting free region . The sensor calibration and constructing convex hull for obstacle detection is recommended in future works to improve the efficiency of the obstacle detection system and classified the free region and obstacle region to create safe avoidance path

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

Achieving a reliable obstacle detection and avoidance system that can provide an effective safe avoidance path for small unmanned aerial vehicle (UAV) is very challenging due to its physical size and weight constraints. Prior works tend to employ the vision based-sensor as the main detection sensor but resulting to high dependency on texture appearance while not having a distance sensing capabilities. The previous system only focused on the detection of the static frontal obstacle without observing the environment which may have moving obstacles. On the other hand, most of the wide spectrum range sensors are heavy and expensive hence not suitable for small UAV. In this work, integration of different based sensors was proposed for a small UAV in detecting unpredictable obstacle appearance situation. The detection of the obstacle is accomplished by analysing the flow field vectors in the image frames sequence. The proposed system was evaluated by conducting the experiments in a real environment which consisted of different configuration of the obstacles. The results from the experiment show that the success rate for detecting unpredictable obstacle appearance is high which is 70% and above. Even though some of the introduced obstacles are considered to have poor texture appearances on their surface, the proposed obstacle detection system was still able to detect the correct appearance movement of the obstacles by detecting the edges