Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm
In recent years, the autonomous mobile multi-robot is popular to assist humans in work such as in the high- r isk environment and during dangerous explorations. The energy of the mobile robot is supplied using the battery. The battery energy is decreasing during the mobile robot’s operation. In o...
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my-upm-ir.699672019-11-12T01:15:53Z Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm 2016-10 Ahmad@Mohd Yusoff, Faisul Arif In recent years, the autonomous mobile multi-robot is popular to assist humans in work such as in the high- r isk environment and during dangerous explorations. The energy of the mobile robot is supplied using the battery. The battery energy is decreasing during the mobile robot’s operation. In order for the mobile robot to work continuously, an autonomous charging system has been developed by the researchers. In the multi-robot environment, various methods have been proposed to avoid the waste of energy when searching for the power station. Application of mobile multi-robot with group and intelligence bio-inspired classified as swarm mobile robot. It is also known as application that applies large number of mobile robot. However, in this thesis, the algorithm based on inspiration of the honeybee’s behavior is improved to manage and improve energy utilization of the mobile multi-robot to emulate the working and foraging behavior. To accomplish this aim, the optimum energy for working and foraging based on the knowledge existence was implemented. Two main types of behavior were simulated in the working mobile robot (doing tasks) and foraging mobile robot (searching for the power station). In both types of behavior, the mobile robots move randomly waypoint. The inspired honeybee’s behavior was designed based on the integration of knowledge of charging station, remaining energy and environment with static and dynamic obstacles. The knowledge of charging station location own by a mobile robot will be shared to other mobile robots. The dynamic threshold of remaining energy was activated when the mobile robot has knowledge of charging station. The improved honeybee inspired algorithm showed that the mobile robot could increase working time efficiency from 37% to 95%. Hence, the increase of energy utilization in the working behavior can increase the working behavior. This thesis has contributed to the efficiency of multiple mobile robots in energy consumption, increase the time and distance in carrying out the work. Mobile robots Robots - Control systems 2016-10 Thesis http://psasir.upm.edu.my/id/eprint/69967/ http://psasir.upm.edu.my/id/eprint/69967/1/ITMA%202016%2015%20IR.pdf text en public doctoral Universiti Putra Malaysia Mobile robots Robots - Control systems |
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Universiti Putra Malaysia |
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PSAS Institutional Repository |
| language |
English |
| topic |
Mobile robots Robots - Control systems |
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Mobile robots Robots - Control systems Ahmad@Mohd Yusoff, Faisul Arif Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| description |
In recent years, the autonomous mobile multi-robot is popular to assist
humans in work such as in the high- r isk environment and during dangerous
explorations. The energy of the mobile robot is supplied using the battery. The
battery energy is decreasing during the mobile robot’s operation. In order for
the mobile robot to work continuously, an autonomous charging system has
been developed by the researchers. In the multi-robot environment, various
methods have been proposed to avoid the waste of energy when searching
for the power station. Application of mobile multi-robot with group and
intelligence bio-inspired classified as swarm mobile robot. It is also known as
application that applies large number of mobile robot. However, in this thesis,
the algorithm based on inspiration of the honeybee’s behavior is improved to
manage and improve energy utilization of the mobile multi-robot to emulate the
working and foraging behavior. To accomplish this aim, the optimum energy
for working and foraging based on the knowledge existence was implemented.
Two main types of behavior were simulated in the working mobile robot (doing
tasks) and foraging mobile robot (searching for the power station). In both types
of behavior, the mobile robots move randomly waypoint. The inspired
honeybee’s behavior was designed based on the integration of knowledge of
charging station, remaining energy and environment with static and dynamic
obstacles. The knowledge of charging station location own by a mobile robot
will be shared to other mobile robots. The dynamic threshold of remaining
energy was activated when the mobile robot has knowledge of charging
station. The improved honeybee inspired algorithm showed that the mobile
robot could increase working time efficiency from 37% to 95%. Hence, the
increase of energy utilization in the working behavior can increase the working
behavior. This thesis has contributed to the efficiency of multiple mobile robots
in energy consumption, increase the time and distance in carrying out the work. |
| format |
Thesis |
| qualification_level |
Doctorate |
| author |
Ahmad@Mohd Yusoff, Faisul Arif |
| author_facet |
Ahmad@Mohd Yusoff, Faisul Arif |
| author_sort |
Ahmad@Mohd Yusoff, Faisul Arif |
| title |
Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| title_short |
Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| title_full |
Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| title_fullStr |
Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| title_full_unstemmed |
Improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| title_sort |
improved autonomous charging of mobile multi-robots using honeybee-inspired algorithm |
| granting_institution |
Universiti Putra Malaysia |
| publishDate |
2016 |
| url |
http://psasir.upm.edu.my/id/eprint/69967/1/ITMA%202016%2015%20IR.pdf |
| _version_ |
1747812738365652992 |