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Trajectory Prediction & Path Planning for an Object Intercepting UAV with a Mounted Depth Camera
Jasper Tan,Arijit Dasgupta,Arjun Agrawal,Sutthiphong Srigrarom 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
A novel control & software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated & simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the on-board autonomy of UAVs intercepting objects.
Fast Drone Detection using SSD and YoloV3
Yew Ji Hao,Lee Koon Teck,Chua Ying Xiang,Enoch Jeevanraj,Sutthiphong Srigrarom 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
This paper aims to introduce the method of detection of high-speed drones using both Single Shot Detector (SSD) and YOLOv3 (You Only Look Once)v3. After conducting experiments and obtaining footage of the fast-flying drones, the software and algorithms are being put to the test. In a motion detector, there are 3 main fundamentals - unmanned aerial vehicle (UAV) detection, UAV identification and tracking of the UAV, which will be introduced as a preliminary UAV detection system to spark of the use of other more advanced image recognition based detector. The alternative of using SSD and YOLOv3 will be the main discussion to target high-speed drones.
Sunlight Compensation for Vision Based Drone Detection
Yan Han Lau,Niven Sie Jun Liang,Shao Xuan Seah,Sutthiphong Srigrarom 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Computer vision based object detection can be applied in security and monitoring scenarios, such as detecting and tracking drone intrusions using cameras. However, its effectiveness is dependent on environmental conditions. For example, under bright sunlight and clear sky conditions, the sunlight reflecting off a target could cause it to blend into the sky and prevent detection. In this paper, an algorithm to compensation for the effects of sunlight on object detection was proposed. The algorithm applied a localised contrast increase to the sky through RGB-HSV conversion and image extraction techniques, which avoided the generation of false positives among the treeline. Preliminary tests with prerecorded videos showed that the algorithm improves detection under bright sunlight conditions but the contrast gain had to be manually tuned. Methods to dynamically tune the gain, and field tests to determine the algorithm’s real time effectiveness, are slated for future work.