“CAM-brain” ATR's billion neuron artificial brain project: A three-year progress report

This paper reports on recent progress made in ATR's attempt to build a 10 000-module evolved neural net artificial brain to control the behavior of a life-sized robot kitten. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997.     

From <Emphasis Type="Italic">q</Emphasis>-cell to artificial brain

This article briefly introduces the artificial-brain-building paradigm developed at the Advanced Telecommunication Research Institute International (ATR). The ATR Artificial Brain Project consists of two related themes: dedicated hardware and psychodynamic architecture. The first theme includes work on the theory of pulsed paraneural networks (PPNN), experiments with q-cellular automata, and studies toward a post-FPGA technology for 3-dimensional evolvable elastic circuits. The second theme involves an original version of the psychodynamic theory of mind, investigating dynamics phenomena in simulated working memories (MemeStorms), testing the behaviors of psychodynamic robots, and building of an environment for growing intelligent systems.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

From bio-inspired vs. psycho-inspired to etho-inspired robots

Artificial intelligence has been roughly divided into two schools of thought since its beginnings, the symbolic (also known as GOFAI, neat, classical, etc.) and the subsymbolic one (connectionist, scruffy, etc.). These two approaches have also had strong influence on the robotics field. In this paper we want to present a less known one, based on Ethology, and its application to the generation of autonomous behavior in mobile robots. In this way, we present the foundations of JDE (Jerarquía Dinámica de Esquemas), an etho-inspired architecture where behavior is organized as a dynamic hierarchy of independent schemas, as well as some examples of its applications, and a discussion about its properties.     

Biologically inspired decision-making in the robot platform MAMoRo

An important step in coming near to building machines with artificial intelligence is by studying and understanding how the human brain works, then applying this knowledge to build machines that “think” using the same concept. MAMoRo (modular autonomous mobile robot) is a general-purpose robot platform targeted at teaching and research in the academia. It consists of three modules: power and motion module, control module, and intelligence module. The decision unit of MAMoRo is distributed into two modules: the control module, which is equipped with a low-cost microcontroller, and handles low-level hardware functions, and the intelligence module, which is equipped with a field-programmable gate array (FPGA) and handles high-level functions. This model of distribution was inspired by the anatomy of the human brain and brings with it many advantages. To prove the concept, MAMoRo was tested with a practical application. This work was presented in part at the First European Workshop on Artificial Life and Robotics, Vienna, Austria, July 12–13, 2007     

Design and implementation of an artificial neuromolecular chip and its applications to pattern classification problems

This paper describes the design and implementation of an artificial neuromolecular model, a biologically motivated architecture, with digital circuit. The artificial neuromolecular circuit (to be referred to as the ANM chip) is an evolvable hardware architecture that combines intra- and inter-neuronal levels of processing. Evolutionary learning algorithm is employed to train the chip for specific tasks. In this paper we applied it to pattern classification problems. Experimental results showed that the ANM chip was capable of learning in an autonomous manner. It also achieved a satisfactory result in pattern differentiation and noise tolerance. An interesting finding was that the chip enjoyed the synergy that percolated through different combinations of evolutionary learning operators.     

Neural networks for control in an artificial brain of a recognition and tracking system

This paper presents a new information-processing machine which is called the artificial brain (ABrain). It also considers the structure of artificial neural networks constructed in a Ricoh neurocomputer RN-2000 in the ABrain to track given trajectories which are produced in a micro-computer or by a light moved by hand in a recognition and tracking system. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita Japan, February 18–20, 1997     

Routine automated synthesis of five patented analog circuits using genetic programming

This article reports on a project in which we browsed patents issued after January 1, 2000 to commercial enterprises or university research institutions for analog electrical circuits. We then employed genetic programming to automatically design (synthesize) entities that duplicated the functionality of five post-2000 issued patents. The automated method works from a high-level statement of the circuits intended function. The article addresses the question of what is actually delivered by the operation of the artificial problem-solving method in relation to the amount of intelligence that is supplied by the humans employing the method (something we refer to as the yield of an automated method). The article also addresses the question of the routineness of the artificial problem-solving method – that is, the amount of effort required to make the transition from problem to problem within a particular domain. The conclusion is that the artificial method routinely delivers high-yield, human-competitive (i.e., previously patented) results.     

Quantum-inspired design of resilient substitution boxes: From coding to hardware implementation

S-boxes constitute a cornerstone component in symmetric-key cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and non-correlated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a twofold objective: first, we evolve regular S-boxes with high non-linearity and low auto-correlation properties; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use a quantum-inspired evolutionary algorithm to optimize regularity, non-linearity and auto-correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit the same algorithm to automatically generate the evolvable hardware designs of substitution boxes that minimize hardware space and encryption/decryption time, which form the two main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through genetic algorithm. We will show that our approach provides higher quality S-boxes coding as well as circuits.     

Autonomous knowledge acquisition based on artificial curiosity: Application to mobile robots in an indoor environment

This paper describes an autonomous system for knowledge acquisition based on artificial curiosity. The proposed approach allows a humanoid robot to discover, in an indoor environment, the world in which it evolves, and to learn autonomously new knowledge about it. The learning process is accomplished by observation and by interaction with a human tutor, based on a cognitive architecture with two levels. Experimental results of deployment of this system on a humanoid robot in a real office environment are provided. We show that our cognitive system allows a humanoid robot to gain increased autonomy in matters of knowledge acquisition.     

The Psikharpax project: towards building an artificial rat

Drawing inspiration from biology, the Psikharpax project aims at endowing a robot with a sensory-motor equipment and a neural control architecture that will afford some of the capacities of autonomy and adaptation that are exhibited by real rats. The paper summarizes the current state of achievement of the project. It successively describes the robot's future sensors and actuators, and several biomimetic models of the anatomy and physiology of structures in the rat's brain, like the hippocampus and the basal ganglia, which have already been at work on various robots, and that make navigation and action selection possible. Preliminary results on the implementation of learning mechanisms in these structures are also presented. Finally, the article discusses the potential benefits that a biologically inspired approach affords to traditional autonomous robotics.     

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.     

FLIP: Prototyping multi-robot systems

The main objective of this article is to promote the use of inexpensive and realistic prototypes to verify the applicability of state-of-the-art technologies in the area of mobile robotics and intelligent manufacturing systems. We propose a toy prototype based on a LEGO^® Mindstorms™ RCX brick extended with a PDA and wireless LAN. We describe and evaluate the application of an agent-oriented approach for a prototype of an autonomous transportation system. This transportation system is an integral part of the FLIP project in which intelligent collaboration during transportation and processing of LEGO^® bricks is investigated.     

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

This paper presents a review on the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the conceptual analysis of “mechatronics”, we analyze the characteristics and control system design requirements of AIMS. In order to fulfill the design requirements, we propose to develop a unified MPC framework for AIMS. The main MPC schemes, covering MPC basics, robust MPC, distributed MPC, Lyapunov-based MPC, event-based MPC, network-based MPC, switched MPC, fast MPC, are reviewed with an attempt to document some of the key achievements in the past decades. Furthermore, we provide the review and analysis of MPC applications to three types of mechatronic systems, including unmanned aerial vehicles (UAVs), autonomous marine vehicles (AMVs), and autonomous ground robots (AGRs). Some promising research directions and concluding remarks are presented.     

Neural robot path planning: The maze problem

The importance of path planning is very significant in the field of robotics. This paper presents the application of multilayer perceptrons to the robot path planning problem, and in particular to the task of maze navigation. Previous published results implied that the training of feedforward multilayered networks failed, because of the non- smoothness of data. Here the path planning problem is reconsidered, and it is shown that multilayer perceptrons are able to learn the task successfully.     

Evolution of neural control structures: some experiments on mobile robots

From perception to action and from action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness or incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is a very important limitation of the evolutionary approach.     

Evolving non-trivial behaviors on real robots: A garbage collecting robot

Recently, a new approach involving a form of simulated evolution has been proposed to build autonomous robots. However, it is still not clear if this approach is adequate for real life problems. In this paper we show how control systems that perform a non-trivial sequence of behaviors can be obtained with this methodology by “canalizing” the evolutionary process in the right direction. In the experiment described in the paper, a mobile robot was successfully trained to keep clear an arena surrounded by walls by locating, recognizing, and grasping “garbage” objects and by taking collected objects outside the arena. The controller of the robot was evolved in simulation and then downloaded and tested on the real robot. We also show that while a given amount of supervision may canalize the evolutionary process in the right direction the addition of unnecessary constraints can delay the evolution of the desired behavior.     

Getting to know each other—Artificial social intelligence for autonomous robots

This paper proposes a research direction to study the development of ‘artificial social intelligence’ of autonomous robots which should result in ‘individualized robot societies’. The approach is highly inspired by the ‘social intelligence hypothesis’, derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development of robots' ‘social relationships’, the investigation of collection and use of body images by means of imitation is proposed. A specific experimental setup which we use to test the theoretical considerations is described. The paper outlines in what kind of applications and for what kind of robot group structures social intelligence might be advantageous.     

An artificial pancreas system in android phones: A dual app architecture

An Artificial Pancreas (AP) system centered around an Android Smartphone is proposed in this paper. This unique architecture involves two separate but interacting modular Android applications (Apps) that are designed for unique functionalities. Even though an artificial pancreas system is a safety-critical system that demands a complex architecture and careful coding process, owing to their modular nature, these apps could be designed and developed through rapid prototyping processes. The first app (App-A), which runs in the front-end, serves as the user interface and acts as a connection hub for the hardware devices, was developed on the Android studio platform. Necessary communication protocols to enable communication with the hardware devices are incorporated into this app. The second app (App-B), which runs in the back-end, is developed using Simulink’s Android support package. It contains a safety-critical model predictive control algorithm with appropriate constraints to compute the required insulin rate, augmented with a Kalman Filter for estimating the states. There is an additional safety logic as well to prevent insulin over-dosage, thereby augmenting to the cause of a safety-critical control algorithm. The contribution of this work is a modular software framework and prototyping methodology that can be used for rapid development, testing and deployment of Android based AP-systems.     

Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

This paper aims to propose a novel design approach for on-line path planning of the multiple mobile robots system with free collision. Based on the artificial bee colony (ABC) algorithm, we propose an efficient artificial bee colony (EABC) algorithm for solving the on-line path planning of multiple mobile robots by choosing the proper objective function for target, obstacles, and robots collision avoidance. The proposed EABC algorithm enhances the performance by using elite individuals for preserving good evolution, the solution sharing provides a proper direction for searching, the instant update strategy provides the newest information of solution. By the proposed approach, the next positions of each robot are designed. Thus, the mobiles robots can travel to the designed targets without collision. Finally, simulation results of illustration examples are introduced to show the effectiveness and performance of the proposed approach.