Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.     

Networked reinforcement learning

Recently, many models of reinforcement learning with hierarchical or modular structures have been proposed. They decompose a task into simpler subtasks and solve them by using multiple agents. However, these models impose certain restrictions on the topological relations of agents and so on. By relaxing these restrictions, we propose networked reinforcement learning, where each agent in a network acts autonomously by regarding the other agents as a part of its environment. Although convergence to an optimal policy is no longer assured, by means of numerical simulations, we show that our model functions appropriately, at least in certain simple situations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli

We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of audiovisual information. The observer adopts the maximum a posteriori approach to estimate the physically delivered position or timing of presented stimuli, simultaneously judging whether they are from the same source or not. Several experimental results on the perception of spatial unity and the ventriloquism effect can be explained comprehensively if the subjects in the experiments are regarded as Bayesian observers who try to accurately locate the stimulus. Moreover, by adaptively changing the inner representation of the Bayesian observer in terms of experience, we show that our model reproduces perceived spatial frame shifts due to the audiovisual adaptation known as the ventriloquism aftereffect.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Identification of chemical oviposition stimulants from rice grain forSitophilus zeamais motschulsky (Coleoptera,Curculionidae)

Chemical components stimulating oviposition bySitophilus zeamais in rice grain were isolated from rice bran and were found to be a mixture of ferulates, diglycerides, and free sterols. Oviposition preference of the species can be induced by synergistic action of these compounds.     

Flexible manipulator inspired by octopus: development of soft arms using sponge and experiment for grasping various objects

In this study, we focus on the intelligent behavior of an octopus and describe the development of a flexible manipulator. To realize the intelligent behavior, we employ sponges, rubbers and wires instead of electrical computers. The manipulator is controlled by the dynamics of the body such as the flexibility of the sponges, resilience of the rubbers and constraint by the wires. To demonstrate the effectiveness of the proposed manipulator, we conducted experiment for grasping various objects. We confirmed that grasping behaviors similar to those of an octopus can be realized by the dynamics of the body without electrical computers.     

Characterization by Stm and Electrical Conductivity of Single-Walled Carbon Nanotubes

Abstract

Single-walled carbon nanotubes (SWCNT) prepared by a DC-Arc-Discharge and purified by a hydrothermal treatment followed by several steps of procedures including heating, burning, extracting and acid-washing are investigated by a conventional STM technique morphologically and spectroscopically. The electical resistivities of compacted pellets (mats) of SWCNT are measured as a function of temperature in the range from the room-temperature to 1.5K. The behaviors seems to be essentially originated in the metallic nature of individual SWCNT.     

Development and field test of the articulated mobile robot T2 Snake-4 for plant disaster prevention

Abstract

In this work, we develop an articulated mobile robot that can move in narrow spaces, climb stairs, gather information, and operate valves for plant disaster prevention. The robot can adopt a tall position using a folding arm and gather information using sensors mounted on the arm. In addition, this paper presents a stair climbing method using a single backward wave. This method enables the robot to climb stairs that have a short tread. The developed robot system is tested in a field test at the World Robot Summit 2018, and the lessons learned in the field test are discussed.     

A control law for vehicle merging inspired by dragonfly behavior

Autonomous control of vehicles has recently attracted considerable attention. In this sense, vehicle merging has become an important topic in this field of research. However, in conventional studies, the controlled vehicle must calculate the movement of other surrounding vehicles to complete the merge, requiring high computational costs. In this paper, we focus on dragonfly behavior to solve this issue. Indeed, insects can behave adaptively in the complex real world in spite of the limited size of their brains. They reduce the computational requirements of their brain by relying on different properties of their surroundings, basing their intelligent behaviors on simple strategies. The behavior of a dragonfly when chasing a prey is an example of these strategies. In this study, we address the vehicle merging maneuver by applying dragonfly’s strategies to control the movement of the merging vehicle. We propose a simple control method inspired by the aforementioned strategies and, finally, we present simulation results that were conducted to demonstrate the effectiveness of this method.