Yuichiro Sueoka

Analysis of implicit control structure in object clustering phenomena

Living things exhibit adaptive and supple locomotion under the real world characterized by rapid changes, high uncertainty, and limited availability of information. But the systematic design of the behavior of living things like ants or bees (e.g. constructing very big and complicated nests) whose brains have very tiny memory abilities, has not been well established. In recent research, this design principle is considered to come from the interaction among the mechanical system (i.e. body), the control law (i.e. brain), and the environment (i.e. real world). To understand these principles with interaction, we propose on Implicit Control Law, which reflects the interaction among the body, the brain, and the environment. In this research, some simple robots named Swiss Robot, Aggregator Robot, Coronoc Robot, are focused. These robots show us interesting object clustering behaviors even if each robot is equipped with simple Explicit Control Law or no Explicit Control Law. From the systematic analyses of clustering behaviors, each Implicit Control Law is formulated. Though the Explicit Control Law and mechanical structures of each robot are exactly different, we can see common parts (principles) of Implicit Control Law. Furthermore, we demonstrate the correspondance between the Explicit Control Law and its clustering ability of each robot (e.g. number of clusters, clustering position).

On Heterogeneity in Foraging by Ant-Like Colony: How Local Affects Global and Vice Versa

In this paper, we discuss influence of heterogeneity in ant-like colony, i.e., how the ratio of individuals (ants) obeying two different action rules affects the behavior of whole colony. For this purpose, we focus on the foraging task – searching the field for food sources, transporting the food packets to the nest. The two types of ants include what we call hard-working and lazy ants, and we perform statistical analyses to show that moderate existence of the lazy ants would boost efficient food transportation; in particular, we point out that the lazy ants play as explorer of newly emerged food sources, but also as global sensor to capture global information through their local experience, thanks to their moving-around behavior. Based on these observations, we propose a distributed estimation method of global information, i.e., to estimate the global mixture ratio by local encounter frequency with other lazy ants. Finally, we expand the estimation method to distributed control strategy of the global mixture ratio, called on-line switching strategy, where every ant dynamically alternates its obeying rules from the hard-working to the lazy and vice versa, based on its local encounter experience.

Emergence and motion analysis of 3D quasi-passive dynamic walking by excitation of lateral rocking

Human is capable of adaptive and supple locomotion in the real world characterized by rapid changes, high uncertainly, and limited availability of information. In order to understand the human walking, this work was motivated by the concept of passive dynamic walking robots, which have no actuation or control system except for gravity are capable of stable, human-looking walking. On the other hand, human can produce a stepping motion not only depend on the legs, but also the rotation of the Center of Mass, arm-swing, the motion of the torso and so on. In this paper, a three dimensional quasi-passive dynamic walking provoked by rocking motion in lateral plane has been investigated. The behavioral analyses with the robot experiments show that this robot can walk on a flat ground and a gait speed is related to the period of lateral rocking.

Analysis of Sheepdog-Type Robot Navigation for Goal-Lost-Situation

In the real world, there is a system in which a dog called a sheepdog stimulates part of a flock of sheep that are freely moving to guide them to a goal position. If we consider this system from the perspective of a control problem, it is an interesting control system: one or more sheepdogs, who act as a small number of controllers, are used to indirectly control many sheep that cannot be directly controlled. For this reason, there have been many studies conducted regarding this system; however, these studies have been limited to building numerical models or performing simulation analyses. Very little research has been done on building a working system. The point we wish to emphasise here is that we attempted to build the sheepdog system in as simple a way as possible. For the purpose, we introduce minimal settings for the sheep model and the sheepdog controller. In the process of building and testing an actual system, we noticed “an emergence of blind zone” because the robots possess size, or so-called cases where the objects in the blind zone cannot be observed because the object is in front. Using the existing method, as the number of sheep increases, it becomes impossible to perceive the goal position, i.e., emerge the goal-lost-situation. This results in the guidance task becoming impossible. As clear identification of the goal position is vital for guidance, we propose a method for cases in which the goal position is invisible. Using our method, the robot appropriately selects another object, and sets this object as the new target. We have confirmed through simulations that the proposed method can maintain guidance regardless of the number of sheep.

Manipulation of the entire group navigation based on dynamic goal-preference switching

In this paper, we propose a brand-new navigation strategy for artificial creatures, such as autonomous mobile robots. The novelty of our proposed method is in group decision-making, based on majority consensus and dynamic switching of individual goal-preferences. First, we introduce a group model with three subpopulations: one subgroup is designed to have the preference of one goal, and the second subgroup is designed to have the preference of another goal. However, the third subgroup (i.e., normal-type robots) is designed to only herd nearby robots. This paper focuses on the role of the normal-type robots and their challenges in controlling the entire group’s destination when giving them goal preferences. For this purpose, we examine the magnitude of goal-preference addition to the third subgroup and the proportion of normal-type robots for the entire group. Throughout our statistical analysis, our results indicate group navigation was successful when there were appropriate settings and dynamic goal-preference additions for a small number of normal-type robots.

Gait Analysis of 6-Legged Robot with Actuator-Equipped Trunk and Insect Inspired Body Structure

Not only 4-legged animals but also 6-legged creatures exhibit a range of gaits. To reveal the mechanism underlying the gait of animals and realize such a gait in a multi-legged robot, it is important to understand the inter-limb coordination. It is possible that the inter-limb coordination is strongly influenced by the structure of trunk to which each leg is connected. In this study, we designed a brand-new insect-inspired trunk mechanism that is equipped with an actuator. Using this trunk, we built a 6-legged robots with passive limbs. We performed walking experiments with the developed robot and analyzed its gait, especially the relationship between the structure of the trunk and the walking velocity. The experimental result shows an insect inspired body structure affects its gait and the walking velocity of 6-legged walking robot.