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Figure 19 The top and bottom views of the implemented ClimateRobo robot (see online version for colours) Figure 20 The reaction of implemented robot under various environmental situations: (a) weather status is good; (b) weather status is normal and (c) weather status is emergency (see online version for colours)

Figure 19 The top and bottom views of the implemented ClimateRobo robot (see online version for colours) Figure 20 The reaction of implemented robot under various environmental situations: (a) weather status is good; (b) weather status is normal and (c) weather status is emergency (see online version for colours)

Related Figures (14)

‘igure 2 The high-level operational flow of the proposed robot system
‘igure 2 The high-level operational flow of the proposed robot system
Figure 3 Block diagram of the designed ClimateRobo robot
Figure 3 Block diagram of the designed ClimateRobo robot
Figure 13 Flow chart of the defined techniques in the proposed ClimateRobo robot
Figure 13 Flow chart of the defined techniques in the proposed ClimateRobo robot
ble 5 Simulation results of the proposed supervised machine learning in 100 cycles (continued)
ble 5 Simulation results of the proposed supervised machine learning in 100 cycles (continued)
Comparison results of the proposed fuzzy decision system for various weather condition and light intensity are illustrated in Figure 16. LI indicates light intensity of the six environments which are utilised in the simulation process. In the results, LI= 10 lux specifies dark limit under clear sky, LI=100 lux indicates very dark overcast day, LI = 1000 lux represents overcast day related to typical TV
Comparison results of the proposed fuzzy decision system for various weather condition and light intensity are illustrated in Figure 16. LI indicates light intensity of the six environments which are utilised in the simulation process. In the results, LI= 10 lux specifies dark limit under clear sky, LI=100 lux indicates very dark overcast day, LI = 1000 lux represents overcast day related to typical TV
Figure 14 The graphical representation of the simulation results produced for the proposed supervised machine learning: (a) the initial instances of the training set and (b) all of the solutions and the best solution (see online version for colours) studio lighting, LI= 10,000 specifies full daylight, and LI = 50,000 as well as LI = 100,000 indicate direct sunlight. The simulation results illustrate that when weather condition decreases and light intensity increases in the simulation procedure, the speed of DC motors will be enhanced accordingly. This is the main goal of proposed fuzzy system to determine the speed of motors based on the values of these parameters. As shown in the results, light intensity has a high impact on the value of speed along the simulation process.
Figure 14 The graphical representation of the simulation results produced for the proposed supervised machine learning: (a) the initial instances of the training set and (b) all of the solutions and the best solution (see online version for colours) studio lighting, LI= 10,000 specifies full daylight, and LI = 50,000 as well as LI = 100,000 indicate direct sunlight. The simulation results illustrate that when weather condition decreases and light intensity increases in the simulation procedure, the speed of DC motors will be enhanced accordingly. This is the main goal of proposed fuzzy system to determine the speed of motors based on the values of these parameters. As shown in the results, light intensity has a high impact on the value of speed along the simulation process.
6 Implementation results
6 Implementation results
Figure 17 Simulation results of the proposed obstacle detection mechanism (see online version for colours)
Figure 17 Simulation results of the proposed obstacle detection mechanism (see online version for colours)
Related topics:
RoboticsArtificial IntelligenceMachine LearningMobile RoboticsAutonomous RoboticsRobotics NavigationMechatronics & RoboticsMechatronics and RoboticsAutonomous RobotsAutonomous Mobile Robots
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