Self-organized task allocation to sequentially interdependent tasks in swarm robotics

2012 ◽  
Vol 28 (1) ◽  
pp. 101-125 ◽  
Author(s):  
Arne Brutschy ◽  
Giovanni Pini ◽  
Carlo Pinciroli ◽  
Mauro Birattari ◽  
Marco Dorigo
Keyword(s):  
Task Allocation ◽  
Swarm Robotics ◽  
Self Organized

scholarly journals A Study on Various Task-Work Allocation Algorithms in Swarm Robotics

2020 ◽  
Vol 2 (2) ◽  
pp. 123-134
Author(s):  
Annamalai .L ◽  
Mohammed Siddiq. M ◽  
Ravi Shankar. S ◽  
Vigneshwar .S
Keyword(s):  
Mobile Robots ◽  
Swarm Intelligence ◽  
Task Allocation ◽  
Swarm Robotics ◽  
Entire System ◽  
Complex Tasks ◽  
Work Allocation ◽  
Allocation Algorithms

This paper discusses the various task allocation algorithms that have been researched, analyzed, and used in swarm robotics. The main reason for switching over to swarm robotics from ordinary mobile robots is because of its ability to perform complex tasks co-operatively with other bots rather than individually. Furthermore, they can be scaled to perform any kind of tasks. To carry out tasks like foraging, surveying and other such tasks that require swarm intelligence, task allocation plays an important role. It is the crux of the entire system and plays a huge role in the success of the implementation of swarm robotics. Few algorithms that address this task allocation have been briefly discussed here.

scholarly journals Achieving task allocation in swarm intelligence with bi-objective embodied evolution

2021 ◽  
Author(s):  
Qihao Shan ◽  
Sanaz Mostaghim
Keyword(s):  
Random Walk ◽  
Swarm Intelligence ◽  
Task Allocation ◽  
Fitness Function ◽  
Pareto Frontier ◽  
Weighted Sum ◽  
Objective Functions ◽  
Nsga Ii ◽  
Self Organized ◽  
Baseline Algorithm

AbstractIn this paper, we seek to achieve task allocation in swarm intelligence using an embodied evolutionary framework, which aims to generate divergent and specialized behaviors among a swarm of agents in an online and self-organized manner. In our considered scenario, specialization is encouraged through a bi-objective composite fitness function for the genomes, which is the weighted sum of a local and a global fitness function. The former depends only on the behavior of an agent itself, while the latter depends on the effectiveness of cooperation among all nearby agents. We have tested two existing variants of embodied evolution on this scenario and compared their performances against those of an individual random walk baseline algorithm. We have found out that those two embodied evolutionary algorithms have good performances at the extreme cases of weight configurations, but are not adequate when the two objective functions interact. We thus propose a novel bi-objective embodied evolutionary algorithm, which handles the aforementioned scenario by controlling the proportion of specialized behaviors via a dynamic reproductive isolation mechanism. Its performances are compared against those of other considered algorithms, as well as the theoretical Pareto frontier produced by NSGA-II.

Sign in / Sign up

Export Citation Format

Share Document