The blockchain: a new framework for robotic swarm systems

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications

Investigating the Effect of Intrinsic Motivation on Alpha Desynchronization Using Sample Entropy

The effect of motivation and attention could play an important role in providing personalized learning services and improving learners toward smart education. These effects on brain activity could be quantified by EEG and open the path to analyze the efficiency of services during the learning process. Many studies reported the appearance of EEG alpha desynchronization during the attention period, resulting in better cognitive performance. Motivation was also found to be reflected in EEG. This study investigated the effect of intrinsic motivation on the alpha desynchronization pattern in terms of the complexity of time series data. The sample entropy method was used to quantify the complexity of event-related spectral perturbation (ERSP) of EEG data. We found that when participants can remember the stimulus, ERSP was significantly less complex than when they cannot. However, the effect of intrinsic motivation cannot be defined by using sample entropy directly. ERSP’s main effect showed that motivation affects the complexity of ERSP data; longer continuous alpha desynchronization patterns were found when participants were motivated. Therefore, we introduced an algorithm to identify the longest continuous alpha desynchronization pattern. The method allowed us to understand that intrinsic motivation has an effect on recognition at the frontal and left parietal area directly

iDoRNA: An Interacting Domain-based Tool for Designing RNA-RNA Interaction Systems

RNA-RNA interactions play a crucial role in gene regulation in living organisms. They have gained increasing interest in the field of synthetic biology because of their potential applications in medicine and biotechnology. However, few novel regulators based on RNA-RNA interactions with desired structures and functions have been developed due to the challenges of developing design tools. Recently, we proposed a novel tool, called iDoDe, for designing RNA-RNA interacting sequences by first decomposing RNA structures into interacting domains and then designing each domain using a stochastic algorithm. However, iDoDe did not provide an optimal solution because it still lacks a mechanism to optimize the design. In this work, we have further developed the tool by incorporating a genetic algorithm (GA) to find an RNA solution with maximized structural similarity and minimized hybridized RNA energy, and renamed the tool iDoRNA. A set of suitable parameters for the genetic algorithm were determined and found to be a weighting factor of 0.7, a crossover rate of 0.9, a mutation rate of 0.1, and the number of individuals per population set to 8. We demonstrated the performance of iDoRNA in comparison with iDoDe by using six RNA-RNA interaction models. It was found that iDoRNA could efficiently generate all models of interacting RNAs with far more accuracy and required far less computational time than iDoDe. Moreover, we compared the design performance of our tool against existing design tools using forty-four RNA-RNA interaction models. The results showed that the performance of iDoRNA is better than RiboMaker when considering the ensemble defect, the fitness score and computation time usage. However, it appears that iDoRNA is outperformed by NUPACK and RNAiFold 2.0 when considering the ensemble defect. Nevertheless, iDoRNA can still be an useful alternative tool for designing novel RNA-RNA interactions in synthetic biology research. The source code of iDoRNA can be downloaded from the site http://synbio.sbi.kmutt.ac.th