Autonomous task allocation by artificial evolution for robotic swarms in complex tasks

2018 ◽  
Vol 24 (1) ◽  
pp. 127-134 ◽  
Author(s):  
Yufei Wei ◽  
Motoaki Hiraga ◽  
Kazuhiro Ohkura ◽  
Zlatan Car
Keyword(s):  
Task Allocation ◽  
Artificial Evolution ◽  
Complex Tasks ◽  
Robotic Swarms

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.

Cohesion and segregation in swarm navigation

Robotica ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 209-223 ◽  
Author(s):  
Vinicius Graciano Santos ◽  
Luiz Chaimowicz
Keyword(s):  
Collision Avoidance ◽  
Biological Systems ◽  
Potential Fields ◽  
Basic Requirement ◽  
Complex Tasks ◽  
Safe Navigation ◽  
Artificial Potential Fields ◽  
Robotic Swarms ◽  
Different Types ◽  
Large Groups

SUMMARYThe use of large groups of robots in the execution of complex tasks has received much attention in recent years. Generally called robotic swarms, these systems employ a large number of simple agents to perform different types of tasks. A basic requirement for most robotic swarms is the ability for safe navigation in shared environments. Particularly, two desired behaviors are to keep robots close to their kin and to avoid merging with distinct groups. These are respectively called cohesion and segregation, which are observed in several biological systems. In this paper, we investigate two different approaches that allow swarms of robots to navigate in a cohesive fashion while being segregated from other groups of agents. Our first approach is based on artificial potential fields and hierarchical abstractions. However, this method has one drawback: It needs a central entity which is able to communicate with all robots. To cope with this problem, we introduce a distributed mechanism that combines hierarchical abstractions, flocking behaviors, and an efficient collision avoidance mechanism. We perform simulated and real experiments to study the feasibility and effectiveness of our methods. Results show that both approaches ensure cohesion and segregation during swarm navigation.

Enhancing Learning, Performance, and Adaptability for Complex Tasks

2005 ◽  
Author(s):  
Steve W. J. Kozlowski ◽  
◽  
Richard P. DeShon
Keyword(s):  
Learning Performance ◽  
Complex Tasks
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