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Process of multi agent and situation awareness agent in avoidance of obstacles and scenarios for re-planning the current path.
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... order to verify that modeling process, we created a mission scenario and confirmed through simulation that the mission information transfer between the Multi-Agent Process and the platform is well done in the interface. Figure 7 shows the process of Multi-Agent Process and Situation Awareness Agent in case of avoidance of obstacles, and shows scenarios in which the existing route is re-planned. Figure 8 shows the simulation execution screen of the interface de- veloped in this study. ...
Citations
... UAVs in general carry a payload of cameras, sensors, and devices telemetry for communication between the vehicle to the Ground Control Station (GCS). The figure below is how the USV fish feeder system works which is combined with GCS using 433MHz telemetry so that it can be monitored and run automatically [12,13]. Fig 4. describe about flowchart feeding dispensing, the system starts by waiting for command data from Pixhawk PX4 to Arduino UNO. ...
Fish farming is common in Indonesia. The farmers usually use ponds with a large size for cultivation which have constraints on feeding due to their size. An automatic feed system with An Unmanned Surface Vehicle (USV) can be the solution to overcome this problem. The USV moves to the predetermined position and direction using GPS and compass. When it has arrived at that point this USV feeds the fishes according to the predetermined amount. In this study, the Pixhawk is used to control the USV to accomplish the given mission. It uses mission planner software for determination of the waypoint points, the calibration of components, and the control of PID. The Pixhawk also serves as a ground control station (GCS) for monitoring the motion and position of the USV using radio communication of 433MHz telemetry connected to the GCS and computer. The fish feeding system uses a load cell sensor to measure the weight of the feed to be thrown and two servo motors as a feed production system. Both sensor and servo motors are controlled by an Arduino UNO. The feed tank has a maximum capacity of 7.5 liters with 0.41 cm per kilogram buoyant force of the USV. The test was carried out on a pool of 10 × 10 m by placing 4 Waypoints. The USV has an average speed of 0.65 m/s and reaches a total distance of 55.5 m in 88 seconds. Before releasing to the pond, the feed weight is measured using a load cell sensor which has an accuracy of 98.99%. The difference between the set point and the feed released by the USV is very close, where the average error value is about 4.98%. The error value becomes smaller when more weight is set.
... In particular, our framework is developed using the Discrete Event System Specification (DEVS, [50]), a well-established DES formalism that is gaining acceptance with a holistic construct called the Modeling and Simulation Framework. Additionally, it is worth mentioning that DEVS has been successfully used to simulate and analyze other complex systems involving autonomous vehicles (such as, to name a few, [13,15,28,36,48]), taking advantage of its capabilities to conduct the MBSE process and of its versatility to handle multiple resolution modeling levels [49]. ...
