Varatharasan, Vinorth (Cranfield University), Rao, Shuangshuang (Cranfield university), Toutounji, Eric (Cranfield University), Hong, Ju-Hyeon (Cranfield University), Shin, Hyo-Sang (Cranfield University)

Target Detection, Tracking and Avoidance System for Low-Cost UAVs Using AI-Based Approaches

Scheduled for presentation during the Regular Session "Sensor Fusion" (MoD15T2), Monday, November 25, 2019, 15:40−16:00, Room T2

2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), November 25-27, 2019, Cranfield University, Cranfield, UK

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches. The aim of the proposed system is that an ally UAV can either avoid or track an unexpected enemy UAV with a net to protect itself. In this point of view, a simple and robust target detection, tracking and avoidance system is designed. Two open-source tools were used for the aim: a state-of-the-art object detection technique called SSD and an API for MAVLink compatible systems called MAVSDK. The MAVSDK performs velocity control when a UAV is detected so that the manoeuvre is done simply and efficiently. The proposed system was verified with Software in the loop (SITL) and Hardware in the loop (HITL) simulators. The simplicity of this algorithm makes it innovative, and therefore it should be used in future applications needing robust performances with low-cost hardware such as delivery drone applications.