This paper investigates how we can work towards building net-centric swarms of land, sea and air robots working together to accomplish a common goal. The goal is to bring together swarms of robots from all three sectors to safely benefit mankind without increasing dangers in the process. Several platforms are explored for simulation to investigate swarm robotics within heterogeneous environments.
Road conditions during the rains are aggravated, even after frequent reconstruction and maintenance, and are not repaired on time due to lack of information. Potholes cause problems such as over-inflated tires, wheel damage, damage to the underside of the vehicle, collisions and serious accidents. Therefore, accurate detection of potholes is a necessary task to determine the appropriate system of road management strategies. Several efforts have been made to overcome this problem. In this study, a pothole detection method that takes 2D images into account proposes a pothole detection system which uses convolutional neural networks which increased the accuracy significantly and is cost efficient. The information extracted from the proposed method is used to determine the preliminary maintenance of a road management system and to take immediate action on repair and maintenance.
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