Particle swarm optimization (PSO), a new populationbased
algorithm, has recently been used on mul... more Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time. This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star. To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.
DESCRIPTION This work was submitted to the International Journal of Advanced Robotic Systems (172... more DESCRIPTION This work was submitted to the International Journal of Advanced Robotic Systems (1729-8806) and is accepted by the journal and will publish soon. I will add the article after publish.
Recently, more and more researches have been conducted on the multi-robot system by applying bioi... more Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO decreased and it converged on a small region and cannot search the other region, which is causing the premature convergence problem. In this study we have presented a simulated multi-robot search system to overcome the premature convergence problem. Experimental results show that the proposed algorithm has better performance rather than the basic PSO algorithm on the searching task.
Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot... more Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot system one of the problem is to design a system that allow the robot to work within a team to find a target. There are many methods that are used on the multi-robot systems. One of the methods is Particle Swarm Optimization (PSO) that uses a virtual multi-agent search to find a target in a 2 dimensional search space. In this paper we present a multi-search algorithm by modifying the Particle Swarm Optimization algorithm to model an abstracted level theeffects of changing aspects and parameters of the system suchas number of robots.
Abstract: In this study we present a combinatorial optimization method based on particle swarm op... more Abstract: In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.
Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of... more Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that these techniques face to solve such problem. This study presents a comprehensive survey of the various techniques that have been proposed in this domain. As part of this survey, we include a classification of the approaches and identify their methods.
Particle swarm optimization (PSO), a new populationbased
algorithm, has recently been used on mul... more Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time. This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star. To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.
DESCRIPTION This work was submitted to the International Journal of Advanced Robotic Systems (172... more DESCRIPTION This work was submitted to the International Journal of Advanced Robotic Systems (1729-8806) and is accepted by the journal and will publish soon. I will add the article after publish.
Recently, more and more researches have been conducted on the multi-robot system by applying bioi... more Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO decreased and it converged on a small region and cannot search the other region, which is causing the premature convergence problem. In this study we have presented a simulated multi-robot search system to overcome the premature convergence problem. Experimental results show that the proposed algorithm has better performance rather than the basic PSO algorithm on the searching task.
Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot... more Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot system one of the problem is to design a system that allow the robot to work within a team to find a target. There are many methods that are used on the multi-robot systems. One of the methods is Particle Swarm Optimization (PSO) that uses a virtual multi-agent search to find a target in a 2 dimensional search space. In this paper we present a multi-search algorithm by modifying the Particle Swarm Optimization algorithm to model an abstracted level theeffects of changing aspects and parameters of the system suchas number of robots.
Abstract: In this study we present a combinatorial optimization method based on particle swarm op... more Abstract: In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.
Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of... more Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that these techniques face to solve such problem. This study presents a comprehensive survey of the various techniques that have been proposed in this domain. As part of this survey, we include a classification of the approaches and identify their methods.
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algorithm, has recently been used on multi-robot
systems. Although this algorithm is applied to solve many
optimization problems as well as multi-robot systems, it
has some drawbacks when it is applied on multi-robot
search systems to find a target in a search space containing
big static obstacles. One of these defects is premature
convergence. This means that one of the properties of basic
PSO is that when particles are spread in a search space, as
time increases they tend to converge in a small area. This
shortcoming is also evident on a multi-robot search
system, particularly when there are big static obstacles in
the search space that prevent the robots from finding the
target easily; therefore, as time increases, based on this
property they converge to a small area that may not
contain the target and become entrapped in that area.
Another shortcoming is that basic PSO cannot guarantee
the global convergence of the algorithm. In other words,
initially particles explore different areas, but in some cases
they are not good at exploiting promising areas, which will
increase the search time.
This study proposes a method based on the particle swarm
optimization (PSO) technique on a multi-robot system to
find a target in a search space containing big static obstacles.
This method is not only able to overcome the premature
convergence problem but also establishes an efficient
balance between exploration and exploitation and guarantees
global convergence, reducing the search time by
combining with a local search method, such as A-star.
To validate the effectiveness and usefulness of algorithms,
a simulation environment has been developed for conducting
simulation-based experiments in different scenarios
and for reporting experimental results. These experimental
results have demonstrated that the proposed method is
able to overcome the premature convergence problem and
guarantee global convergence.
solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization
problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO
decreased and it converged on a small region and cannot search the other region, which is causing the premature
convergence problem. In this study we have presented a simulated multi-robot search system to overcome the
premature convergence problem. Experimental results show that the proposed algorithm has better performance
rather than the basic PSO algorithm on the searching task.
local search algorithm on the multi-robot search system. Under this method, in order to create a balance between
exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between
target and the robot become less than specific measure then a local search algorithm is performed. The local search
encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental
results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet
the challenges of robotic applications that can be described as optimization problems.
collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed
to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as
the two most important challenges that these techniques face to solve such problem. This study presents a
comprehensive survey of the various techniques that have been proposed in this domain. As part of this
survey, we include a classification of the approaches and identify their methods.
algorithm, has recently been used on multi-robot
systems. Although this algorithm is applied to solve many
optimization problems as well as multi-robot systems, it
has some drawbacks when it is applied on multi-robot
search systems to find a target in a search space containing
big static obstacles. One of these defects is premature
convergence. This means that one of the properties of basic
PSO is that when particles are spread in a search space, as
time increases they tend to converge in a small area. This
shortcoming is also evident on a multi-robot search
system, particularly when there are big static obstacles in
the search space that prevent the robots from finding the
target easily; therefore, as time increases, based on this
property they converge to a small area that may not
contain the target and become entrapped in that area.
Another shortcoming is that basic PSO cannot guarantee
the global convergence of the algorithm. In other words,
initially particles explore different areas, but in some cases
they are not good at exploiting promising areas, which will
increase the search time.
This study proposes a method based on the particle swarm
optimization (PSO) technique on a multi-robot system to
find a target in a search space containing big static obstacles.
This method is not only able to overcome the premature
convergence problem but also establishes an efficient
balance between exploration and exploitation and guarantees
global convergence, reducing the search time by
combining with a local search method, such as A-star.
To validate the effectiveness and usefulness of algorithms,
a simulation environment has been developed for conducting
simulation-based experiments in different scenarios
and for reporting experimental results. These experimental
results have demonstrated that the proposed method is
able to overcome the premature convergence problem and
guarantee global convergence.
solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization
problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO
decreased and it converged on a small region and cannot search the other region, which is causing the premature
convergence problem. In this study we have presented a simulated multi-robot search system to overcome the
premature convergence problem. Experimental results show that the proposed algorithm has better performance
rather than the basic PSO algorithm on the searching task.
local search algorithm on the multi-robot search system. Under this method, in order to create a balance between
exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between
target and the robot become less than specific measure then a local search algorithm is performed. The local search
encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental
results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet
the challenges of robotic applications that can be described as optimization problems.
collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed
to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as
the two most important challenges that these techniques face to solve such problem. This study presents a
comprehensive survey of the various techniques that have been proposed in this domain. As part of this
survey, we include a classification of the approaches and identify their methods.