No abstract

In a scenario where fire accidents takes place the priority is always human safety and acting swiftly to contain the fire from further spreading. The modern autonomous systems can promise both human safety and can perform actions rapidly. One such scenario which is motivated by urban firefighting was designed in challenge 3 of MBZIRC 2020 competition. In this challenge the UAV's and UGV collaborate autonomously to detect the fire and quench the flames with water. So, in this project we have developed Robot Operating System (ROS)-based autonomous system to solve the challenge for UGV criteria by detecting targeted objects in real-time, in our case its simulated fire and red colored softballs. Then finally localize those targets as markers in the map and navigate autonomously to all those targets. This work has two sections, in the first section mapping and localizing the fire and softballs in highly cluttered environment and then reaching those targets autonomously. Robustly mapping the area with adequate sensors and detection of targets with optimally trained CNN based network is the key to localizing of the targeted objects in a highly cluttered environments.