Millions of people are affected by neurological disorders. Brain-Machine Interfaces (BMIs) and neuroprosthesis have been the object of extensive research and may represent a valid treatment for diverse neurological diseases. The realization of neuroprostheses requires technologies to interact with neuronal cell assemblies in the nervous system, and to drive them into a desired state or to produce a specific behavior. One of the best solutions to design this kind of neuroprostheses is to implement a real-time hardware Spiking Neural Network (SNN). Not only will it mimic the electrical behavior of one biological neuron but also the dynamic of a neural network with plasticity rules. In this work, we present digital hardware implementation of biomimetic SNNs for stimulating in vitro cells. By connecting living neurons with electronic SNNs, we are intending to develop a model system for a brain-electronics interface.

In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.

We propose a behavior learning method of a mobile robot using visual information. The proposed system consists of two networks for the state expression and the behavior generation. The state expression network, which is based on self-organizing algorithm, categorizes the robot's states. Also, the behavior generation network acquires the robot's behaviors using the output of the state expression network. Here, for training the behavior generation network, we utilize the actor-critic method as a kind of unsupervised learning scheme. We report some experimental results using the mobile robot in real environment. The mobile robot generates adaptive behaviors utilizing visual information.

This paper discusses the problems in teleoperation systems for mobile robots and the use of a virtual world in these systems. In order to achieve smooth operation of the mobile robot via the communication link, we should consider the delays in data transfer. To compensate the incomplete data sets, virtual images can be generated by computer graphics when the information of the working environment can be acquired beforehand. We construct a virtual world teleoperation system. The performance of the system is examined through experiments with an actual mobile robot. It is shown that the virtual robot can be operated by a human operator in almost the same manner as a teleoperated real robot.

We have proposed the collision avoidance methods in a multirobot system based on the information exchanged by the "LOCISS: Locally Communicable Infrared Sensory System", which is developed by the authors. One of the problems in the LOCISS based methods is that the number of situations which should be considered increases very much when the number of the robots and stationary obstacles in the working environment increases. In order to reduce the required computational power and memory capacity for such a large number of situations, we propose, in this paper, a multilayered reinforcement learning scheme to acquire appropriate collision avoidance behaviors. The feasibility and the performance of the proposed scheme is examined through the experiment using actual mobile robots.

This paper presents a novel concept of self-organizing collective robots with morphogenesis in a vertical plane. It is potentially applicable to autonomous mobile robots. For physical reconfiguration of a swarm of robots against gravity, new types of mechanisms and control strategies are proposed and demonstrated. Prototype robots have been fabricated in order to confirm the basic feasibility of the mechanisms. Each robot is composed of a body and a pair of arms. The body can be regarded as a cube with edge length of 90 mm, and is equipped with permanent magnets for bonding with another robot. The arms change the bonding configuration by rotating and sliding motions. As for the control strategies, we proposed the algorithms which can locally generate specific global formations of robots, with minimum interactions between neighboring robots. The overall scheme is similar to cellular automata. The control algorithms proposed have been tested by simulations.

Describes a neural network system which executes identification of robot dynamics and controller adaptation in parallel with robot control. The system consists of two parts: real-world part and imaginary-world part. The real-world part is a feedback control system for the actual robot. In the imaginary-world part, the model of robot and the controller are adjusted continuously in order to deal with the change of dynamic property caused by disturbance and so on. The system is designed to be suitable for a computer system with parallel processing ability. In the paper, adaptability of the controller system is investigated by heading keeping and path following experiments on the condition that unknown disturbances are given to the robot.

In this paper, we discuss autonomous navigation in multirobot environment based on local communication. To carry out cooperative tasks using multiple autonomous robots, navigation systems should be implemented to the robots because mutual alignment of their positions and collision avoidance to each other become important. Therefore, it seems a collision avoidance and a self-localization are indispensable as elemental techniques for the navigation. Although these techniques need to recognize situation of robots surrounding environment, conventional devices to acquire information around them can not work sufficiently in such environment. For this problem, we propose techniques based on the local communication and the navigation system by combining them for multirobot environment. It is shown that robots can arrive their goal precisely without collision among them through proposed navigation systems.

We discuss adaptive behavior acquisition for collision avoidance among multiple autonomous mobile robots which are equipped with the locally communicable infrared sensory system (LOCISS). The LOCISS is a local sensing device for collision avoidance by which robots can detect other robots and obstacles and discriminate them by exchanging relevant information. We (1996) reported previously a collision avoidance method between two robots based on the predetermined rules using LOCISS. It is, however, difficult to realize collision avoidance among three or more robots by the predetermined rules only because situations around the robots become more complicated as the number of robots increases. Thus, it is desirable for the robots to have an adaptive capability for acquisition of the behaviors to avoid collision with other robots and obstacles. To acquire the adaptive behavior, the reinforcement learning is introduced in this paper. It is shown that appropriate behaviors for collision avoidance can be successfully acquired through the proposed learning process.

This paper proposes a new strategy for knowledge sharing among multiple autonomous mobile robots using newly developed intelligent data carriers (IDCs) to make the multi-robot system more flexible and robust against complex environmental conditions and, to reduce the amount of global communication for mutual exchange of information which is useful locally in a limited area. The IDC is an immobile but portable device (data carrier) for information storage and management with a unique mushroom-like structure to be easily handled by a forklift mechanism which can be mounted on actual mobile robots. A robot can place a unit at a specific location in its working environment and write locally relevant knowledge into the unit. Other robots can share the knowledge which should indicate the existence of specific instruments, objects or topographies, e.g., dead ends, drop-offs, steps, etc., by reading from the placed IDC units. The handling method of the IDC unit is examined through the experiment using an omni-directional mobile robot. It is shown that the robot succeeded in placing the unit at an appropriate location to share the information indicating that a dead end exists.

This paper describes the design and overall structure of a test-bed system for developing multiple autonomous underwater robot systems. The developed system can be regarded as a multi-agent system which consists of a pair of autonomous underwater robots, named "The Twin-Burger-I and II", an ultrasonic positioning system, and a human diver. An ultrasonic command link system is used for communication among each agents. The distributed vehicle management architecture (DVMA) is applied to the Twin-Burger robot as an architecture for the control software of the robot. Mission execution experiments are carried out in the Institute of Industrial Science (IIS) testing pool. The results show the effectiveness and applicability of the DVMA.

A vision based system is developed for interaction between the man-robot unit and robot-robot unit in underwater environments. Vision is the communicating medium between the agents. The image consists of an ON/OFF light pattern produced by a set of electro-luminescent panels, representing a particular command. The vision system introduced in this paper recognizes these patterns under the defined environmental conditions. In the image processing system the Hough transform is performed with the Sobel operator to extract important features of the image. The decision making time of this system is approximately 1 sec on a transputer based hardware, which is equivalent to the CPU system on the testbed robot, "The Twin-Burger". This decision making time is acceptable for slowly moving underwater robots. In order to track the region of interest, the vision system calculates the pan and tilt angles of the CCD camera. The system is tested for different situations underwater as well as in air.