Any kinds of natural disaster are undesirable. Loss and damage are the most experienced as they come. Property and people have to be relieved, and it's not an easy matter. Among the deaths caused by buildings, some may still be alive and need helps as soon as possible, but this is too risky for the rescue team since the location is still in dangerous level. Therefore, we created the detector hexapod robot to replace the tasks of the rescue teams in searching for the victims of the disasters, so there are no more victims from the rescue team. The hexapod robot is a six-legged robot which shapes and runs like a spider. This research focuses on the analysis of the push button switch as a robotic foot control input. This is because walking technique is an effective major factor in navigation of robots. A good method is required to maintain the height of the robot's foot while it is walking. So to solve this, the push button switch application is used along with the inverse kinematics calculations on each routine program in adjusting the position of the end effector on the floor surface. In shifting, the navigation runs well without any failure if the position of the foot does not touch the floor. The test is done in 2 steps, comparing the inverse kinematics calculations with x and y inputs which are applied to the robot program code then comparing the travel time condition by using push button switch and without push button switch. The result of robot in this study can be re-developed in the future, using servos with greater torque and better control input than push button switch.
In this research, a fuzzy logic algorithm is implemented in a monitoring system for detecting the potential fires in peat land. The monitoring system in this research employs two sensors as the fuzzy inputs, i.e. TGS 2600 gas sensor and DHT11 temperature sensor. The outputs of the fuzzy logic are the specified conditions of motor activation (PWM) with 3600 rotations. The system is monitored through camera, which sends the monitoring result to android via web server. The result is sent when TGS 2600 and DHT11 sensors detect the determined gas concentration and surrounding temperature. Before sending the result, the rotating motor stops every five minutes to take the photograph of peat land location. The result shows that the algorithm used in this research has been successful in determining the condition of the peat lands correctly and therefore can be used as the early prevention of fires.
This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed.  At the end of summary, the applications of the SI algorithms are presented.
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