The Future Impacts of Autonomous Aid on Disaster Relief Efforts

Natural disasters and other catastrophes have significantly increased in recent years (Oishi & Komiya, 2017). Currently, resources available to succor in response to cataclysms are limited. Humans save lives, while ultimately risking their own. In order to bypass this risk, autonomous robot programming is essential. Research and advocacy regarding autonomous aid in the scientific community has yet to be fully addressed (Oishi & Komiya, 2017). Because of this, first responders, firefighters, and policemen are perpetually endangered. Furthermore, technological contributions would also eliminate human error, promote productivity, and stimulate collaboration on matters unable to be solved autonomously. A robot prototype, designed to retrieve 6x6х6 inch cubes, was programmed to a controller, but also operated autonomously. Despite the controller being beneficial for specific functions, autonomous programming proved to be advantageous when used applicably. While the initial task was direct, the knowledge acquired from the project possesses the potential to enhance future ventures seeking to aid disaster relief

Control of autonomous robot behavior using data filtering through adaptive resonance theory

Abstract The aim of the article is to use neural networks to control autonomous robot behavior. The type of the controlling neural network was chosen a backpropagation neural network with a sigmoidal transfer function. The focus in this article is put on the use adaptive resonance theory (ART1) for data filtering. ART1 is used for preprocessing of the training set. This allows finding typical patterns in the full training set and thus covering the whole space of solutions. The neural network adapted by a reduced training set has a greater ability of generalization. The work also discusses the influence of vigilance parameter settings for filtering the training set. The proposed approach to data filtering through ART1 is experimentally verified to control the behavior of an autonomous robot in an unknown environment with varying degrees of difficulty regarding the location of obstacles. All obtained results are evaluated in the conclusion