4/10/2023 0 Comments Webots static friction![]() ![]() ![]() In other words, it refers to the application of artificial evolution to generate autonomous robots and/or their controllers with minimal or no direct input from humans.Įvolutionary Computation: refers to a subfield of artificial intelligence or computational intelligence that involves computational algorithms that are inspired by biological processes. Key Terms in this ChapterĮvolutionary Robotics: is a method that utilizes evolutionary computation to automatically synthesize controllers for autonomous robots. As such, robots that are evolved with RF-localization behavior may potentially serve as an ideal SAR assistant (Gibson 2007 Pike, 2007 NOAA 2008). The RF signal source has provided the capability for improvements in tracking, search and rescue efforts. It is a term that refers to an alternating current having characteristics such that, if the current is an input to an antenna, an electromagnetic field is generated suitable for wireless broadcasting and/or communications used (Gibson 2007 Pike, 2007 NOAA 2008). The RF signal is defined as radio frequency signal (abbreviated RF, rf, or r.f.) (Gibson 2007 Pike, 2007 NOAA 2008). In addition, research regarding radio frequency (RF) signal localization has yet to be studied in ER. However, the fitness functions used can be further improved or augmented in order to increase the robot’s ability in completing more complex tasks (Marco, 1996). Besides, the literature also showed that other researchers have successfully synthesized some fitness functions to evolve the robots for the required behaviors (Floreano, 1996 Floreano, 2000a Floreano, 2000b Nolfi, 2000). Additionally, to the best of our knowledge, there have not been any studies conducted yet in applying the multi-objective algorithm in evolving the robot controllers for phototaxis behavior. They have not emphasized on the relationship between the robot’s behavior and its corresponding hidden neurons. In previous studies related to phototaxis tasks, the researchers used a fixed amount of hidden neurons in the neural network or just a two-layer neural network for their robot’s task (Floreano, 1996 Floreano, 2000b Teo, 2004b). Using different approaches of evolution, for example Genetic Algorithm, Genetic Programming, Co-evolution, and anytime learning, researchers strive for an algorithm that is able to train robots to perform their tasks without external supervision or help.Ī number of studies have already been successfully conducted in evolutionary robotics for phototaxis, phonotaxis and obstacle avoidance tasks (Floreano, 2000 Teo, 2005 Floreano, 1996 Floreano, 2000a Horchler, 2004 Reeve, 2005). The evolutionary processes involve a set of operators, namely selection, crossover, mutation and other genetic algorithm (GA) operators (Coello, 2005a Floreano, 2000a). Algorithms in ER frequently operate on a population of candidate controllers, initially selected from some random distributions (Alba, 2002 Urzelai, 2000). ER is mainly seen as a strategy to develop more complex robot controllers (Floreano, 1996 Floreano, 2000b). In other words, ER is defined as the synthesis of autonomous robots and/or controllers using artificial evolutionary methods (Teo, 2004a Teo, 2005). ![]() Understanding the underlying assumptions and theoretical constructs through the utilization of EMO will allow the robotics researchers to better design autonomous robot controllers that require minimal levels of human-designed elements.Įvolutionary Robotics (ER) is a methodology to develop a suitable control system for autonomous robots with minimal or without human intervention at all through evolutionary computation, to adapt itself to partially unknown or dynamic environments (Floreano, 2000b Nelson, 2006 Pratihar, 2003). The experimentation results showed the controllers allowed the robots to navigate successfully, hence demonstrating the EMO algorithm can be practically used to automatically generate controllers for phototaxis and RF-localization behaviors, respectively. Furthermore, the controllers’ moving performances, tracking ability and robustness also have been demonstrated and tested with four different levels of environments. ![]() It explains the comparison performances among the elitism without archive and elitism with archive used in the evolutionary multi-objective optimization (EMO) algorithm in an evolutionary robotics study. AbstractThe utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |