Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

2018 ◽  
Vol 22 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Motoaki Hiraga ◽  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Swarm Robotics ◽  
Local Environment ◽  
Coarse Grained ◽  
Neural Network Controller ◽  
Network Controller ◽  
Robotic Swarm ◽  
Limited Sensing

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

Analysis of the High-Speed Network Performance through a Prediction Feedback Based Model

2012 ◽  
pp. 162-178
Author(s):  
Manjunath Ramachandra ◽  
Pandit Pattabhirama
Keyword(s):  
Wireless Networks ◽  
High Speed ◽  
Network Performance ◽  
Data Transfer ◽  
Time Data ◽  
Neural Network Controller ◽  
High Speed Network ◽  
Network Controller ◽  
And Control

Performance modeling of a high speed network is challenging, especially when the size of the network is large. The high speed networks span various applications such as the transportation, wireless sensors, et cetera. The present day transportation system makes uses of Internet for efficient command and control transfers. In such a communication system, reliability and in-time data transfer is critical. In addition to the sensor information, the present day wireless networks target to support streaming of multimedia and entertainment data from mobile to infrastructure network and vice versa. In this chapter, a novel modeling method for the network and its traffic shaping is introduced, and simulation model is provided. The performance with this model is analyzed. The case-study with wireless networks is considered. The chapter is essentially about solving the congestion control of packet loss using a differentially fed neural network controller.

Effects of Congestion on Swarm Performance and Autonomous Specialization in Robotic Swarms

2019 ◽  
Vol 31 (4) ◽  
pp. 526-534 ◽  
Author(s):  
Motoaki Hiraga ◽  
◽  
Kazuhiro Ohkura
Keyword(s):  
Computer Simulation ◽  
Collective Behavior ◽  
Swarm Robotics ◽  
Autonomous Robots ◽  
Evolutionary Robotics ◽  
Centralized Control ◽  
Robot Controller ◽  
Robotic Swarms ◽  
Robotic Swarm ◽  
The Relationship

This paper focuses on the effect of congestion on swarm performance by considering the number of robots and their size. Swarm robotics is the study of a large group of autonomous robots from which collective behavior emerges without reliance on any centralized control. Due to the fact that robotic swarms are composed of a large number of robots, it is important to consider the congestion among them. However, only a few studies have focused on the relationship between the congestion and the performance of robotic swarms; moreover, these studies only discuss the effect of the number of robots. In this study, experiments were conducted by computer simulation and carried out by varying both the number of robots and the size of the robots in a path formation task. The robot controller was designed with an evolutionary robotics approach. The results show that not only the number of robots but also their size are essential features in the relationship between congestion and swarm performance. In addition, autonomous specialization within the robotic swarm emerged in situations with moderate congestion.

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