This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

In this paper, we proposed an enhanced path planning strategy for sweeper robots, which were created for the curling Olympic games. The main task for the multi-robot system is to clean the ice surface making a smooth path for a curling stone. The sweeping robots should have a motion planning on how to follow the curling stone slide and to prevent any collisions. In order to find the next position of the sweeping robot, it needs to establish the current position and to compute the next position of the curling stone. The initial and goal points of the sweeping robots are found and set up based on the simulation results from the main server. While the curling stone moves, the sweeping robots measure its position and adjust their motions according to the stone position trajectory. If the distance between the current and the next positions of a curling stone exceeds the threshold value, the sweeping robots should activate the sweeping mechanism preventing collisions with the stone. Since the estimation of the sweeping robot motion solely depends on the stone’s trajectory, the accumulation of errors is undesirable. Thus, the stone trajectory should be recalculated in a certain time step using the trend-adjusted exponential smoothing method. Then, the formation of the sweeping robot system can be calibrated according to the stone path computation. The obtained experimental results proved the efficiency of the proposed path planning method.

This paper presents an image-processing technique for cabin delivery employing local localization and docking in a mobile station, which is a mobile vertiport for the use of dual-mode flying cars. A dual-mode flying automobile with an aerial electric vehicle (AEV), a ground electric vehicle (GEV), and a cabin is a future method of transportation that can be used in both the air and on the ground. To enable AEVs to land in a specific position, a landing site is necessary. The proposed AEV uses vertical take-off and landing, and a vertiport landing site is required. As vertical take-off and landing sites require a lot of space and are challenging to operate in multiple positions, we suggest a mobile vertiport that can fit into a small space. A mobile station is appropriate for dual-mode flying cars since it includes critical activities such as transporting AEVs from the ground and charging as well as a cabin-delivery system. The mobile station can generate a path to the AEV by calculating the relative position using the markers attached to the AEV and estimating the position of the landing AEV. The mobile station detects a marker for precise positioning correction, followed by exact position correction for cabin delivery, to travel to the accurate position of the AEV. To increase the success rate of cabin delivery, docking markers are identified and the angle position error between the mobile station and cabin is computed and corrected to rectify the position between the cabin and the mobile station for cabin delivery. In addition, the experimental results revealed a mechanically correctable error range that encompassed all experimental values. Consequently, this study showed that image processing may be used to create a mobile station for dual-mode flying automobiles.

A water distribution system is an essential part of urban water management, as it ensures reliable water supply to residents. In order to monitor water distribution systems and prevent several problems, pressure sensors are an important part of the infrastructure, which should be examined frequently. The purpose of this study is to propose a method for evaluating pressure sensors in existing water distribution systems based on data analysis. Water distribution system in Yeonam District, Daegu Metropolitan City, was used in this study. We obtained field pressure data using a low accuracy field pressure sensor. Two different types of data were collected: daily data and high frequency data from transient events. Analyzing and evaluating field pressure data was based on information theory, which quantifies entropy as an indication of the amount of information in a signal. The best sampling frequency for a pressure sensor can be determined by determining data feasibility in transient data and the optimal sampling frequency for daily data.