Wrinkle generation in shear-enforced rectangular membrane

The objective of this study is to clarify the wrinkle behavior of a flat rectangular membrane undergoing shear displacement. To achieve this goal, an equilibrium path tracking method using a finite element method is developed. This method includes a bifurcation path tracking analysis that searches for bifurcation solutions. This method establishes an image of the membrane behavior by calculating a series of successive equilibrium states before and after bifurcation buckling. Generally in experiments, flat rectangular membranes have shear displacement imposed on top or bottom edges, while the left and right sides have free boundaries. At large values of shear displacement, the wrinkles cover the entire membrane, but the free boundaries result in uneven shapes and distributions. Further increase in shear results in small wrinkles, referred to as collapsed sections, generated on existing wrinkles. As collapsed sections grow, new wrinkles are generated. However, the universality of this wrinkle generation mechanism may be affected by the free boundaries. By applying cyclic boundary conditions, effects of free boundaries, which include uneven wrinkle shape and distribution, can be eliminated. In addition, by changing the membrane aspect ratio, the effects of geometry are also evaluated. For all membranes, the wrinkle generation from collapsed sections is observed and its independence from free boundaries and aspect ratio is shown. By analyzing stress and displacement fields, the formation of collapsed sections is explained. In addition, for the cyclic boundary conditions, the change in aspect ratio results in almost the same bifurcation structure. Therefore, the wrinkle behavior evaluation in this study can be useful in predicting wrinkle behavior

Wrinkle Generation Without Bifurcation in a Shear-Enforced Rectangular Membrane with Free Boundaries

The object of this study is to clarify wrinkling behavior of a shear-enforced flat rectangular membrane with free boundaries. For this purpose, an equilibrium path tracking method using a finite element method is developed. This method includes bifurcation path tracking analysis that searches for bifurcation solutions. This method establishes an image of membrane behavior by calculating a series of successive equilibrium states before and after bifurcation buckling. Through detailed analysis of stress, displacement fields, and wrinkle interaction over a load parameter range, the analysis shows how existing wrinkles affect each other and the generation of new wrinkles. As a result, there is wrinkle generation with bifurcation and without bifurcation. The wrinkle generation mechanism without bifurcation is analyzed in detail. Wrinkle generation caused by bifurcations could potentially result in a large number of equilibrium paths. Each equilibrium path represents a specific wrinkle pattern. However, the analyzed results show that significantly fewer equilibrium paths are obtained than expected. These are due to wrinkle generation without bifurcations and to deformation symmetry

Truss Assembly by Space Robot and Task Error Recovery via Reinforcement Learning

This paper addresses an experimental system simulating a free-flying space robot, which has been constructed to study autonomous space robots. The experimental system consists of a space robot model, a frictionless table system, a computer system, and a vision sensor system. The robot model composed of two manipulators and a satellite vehicle can move freely on a two-dimensional planar table without friction by using air-bearings. The robot model has successfully performed the automatic truss structure construction including many jobs, e.g., manipulator berthing, component manipulation, arm trajectory control avoiding collision, assembly considering contact with the environment, etc. Moreover, even if the robot fails in a task planned in advance, the robot accomplishes it by task re-planning through reinforcement learning. The experiment demonstrates the possibility of the automatic construction and the usefulness of space robot

Autonomous Environment Recognition by Robotic Manipulators

This paper discusses methods of autonomus environment recognition and action by a robotic manipulator working with dynamic interaction to the enviroment, e.g., assembling. A method automatically recognizes the contacting situation with the work site from the sensor outputs and the robotic manipulator motion. The autonomous recognition then discriminates the constraint conditions at manopulator hand using the self-organizing map that is a kind of unsupervisedlearning of neural networks. The discrimination of the constraint conditions is successfully demonstraed by a numerical simulation of a 3-link SCARA type manipulator. Another is for the cognitive action. Some approaches based on the reinforcement learnin are proposed. They give models of cognitive actions and aproaches to so-called frame problem obstructing efficient learning and action

VPP control cannot stabilize the posture during walking for high VPP location

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P7

Reinforcement learning accelerated by using state transition model with robotic applications

金沢大学工学部This paper discusses a method to accelerate reinforcement learning. Firstly defined is a concept that reduces the state space conserving policy. An algorithm is then given that calculates the optimal cost-to-go and the optimal policy in the reduced space from those in the original space. Using the reduced state space, learning convergence is accelerated. Its usefulness for both DP (dynamic programing) iteration and Q-learning are compared through a maze example. The convergence of the optimal cost-to-go in the original state space needs approximately N or more times as long as that in the reduced state space, where N is a ratio of the state number of the original space to the reduced space. The acceleration effect for Q-learning is more remarkable than that for the DP iteration. The proposed technique is also applied to a robot manipulator working for a peg-in-hole task with geometric constraints. The state space reduction can be considered as a model of the change of observation, i.e., one of cognitive actions. The obtained results explain that the change of observation is reasonable in terms of learning efficiency

A study toward cognitive action with environment recognition by a learning space robot

金沢大学工学部泉田, 啓This paper addresses an experimental system simulating a free-flying space robot, which has been constructed to study autonomous space robots. The experimental system consists of a space robot model, a frictionless table system, a computer system, and a vision sensor system. The robot model is composed of two manipulators and a satellite vehicle, and can move freely on a two-dimensional planar table, without friction, using air-bearings. The robot model has successfully performed the automatic truss structure assembly, including many jobs, e.g., manipulator berthing, component manipulation, arm trajectory control collision avoidance, assembly using force control, etc. Moreover, even if the robot fails in a task planned in advance, the robot re-plans the task by using reinforcement learning, and obtains the task goal for basically kinematic problems. But, for a class of complicated dynamic problems, the computational periods and efforts are infeasible for on-line learning. Some approaches are proposed to accelerate the learning speed, which also give models of cognitive actions and approaches to so-called a frame problem. The experiment demonstrates the possibility of the autonomous construction and the usefulness of space robots

Fractal mechanism of basin of attraction in passive dynamic walking

Passive dynamic walking is a model that walks down a shallow slope without any control or input. This model has been widely used to investigate how humans walk with low energy consumption and provides design principles for energy-efficient biped robots. However, the basin of attraction is very small and thin and has a fractal-like complicated shape, which makes producing stable walking difficult. In our previous study, we used the simplest walking model and investigated the fractal-like basin of attraction based on dynamical systems theory by focusing on the hybrid dynamics of the model composed of the continuous dynamics with saddle hyperbolicity and the discontinuous dynamics caused by the impact upon foot contact. We clarified that the fractal-like basin of attraction is generated through iterative stretching and bending deformations of the domain of the Poincaré map by sequential inverse images. However, whether the fractal-like basin of attraction is actually fractal, i.e., whether infinitely many self-similar patterns are embedded in the basin of attraction, is dependent on the slope angle, and the mechanism remains unclear. In the present study, we improved our previous analysis in order to clarify this mechanism. In particular, we newly focused on the range of the Poincaré map and specified the regions that are stretched and bent by the sequential inverse images of the Poincaré map. Through the analysis of the specified regions, we clarified the conditions and mechanism required for the basin of attraction to be fractal

Autonomous Robust Skill Generation Using Reinforcement Learning with Plant Variation

This paper discusses an autonomous space robot for a truss structure assembly using some reinforcement learning. It is difficult for a space robot to complete contact tasks within a real environment, for example, a peg-in-hole task, because of error between the real environment and the controller model. In order to solve problems, we propose an autonomous space robot able to obtain proficient and robust skills by overcoming error to complete a task. The proposed approach develops skills by reinforcement learning that considers plant variation, that is, modeling error. Numerical simulations and experiments show the proposed method is useful in real environments

Modeling and emergence of flapping flight of butterfly based on experimental measurements

The objective of this paper is to clarify the principle of stabilization in flapping-of-wing flight of a butterfly, which is a rhythmic and cyclic motion. For this purpose, a dynamics model of a butterfly is derived by Lagrange’s method, where the butterfly is considered as a rigid multi-body system. For the aerodynamic forces, a panel method is applied. Validity of the mathematical models is shown by an agreement of the numerical result with the measured data. Then, periodic orbits of flapping-of-wing flights are searched in order to fly the butterfly models. Almost periodic orbits are obtained, but the model in the searched flapping-of-wing flight is unstable. This research, then, studies how the wake-induced flow and the flexibly torsional wing’s effect on the flight stability. Numerical simulations demonstrate that both the wake-induced flow and the flexible torsion reduces the flight instability. Because the obtained periodic flapping-of-wing flight is unstable, a feedback control system is designed, and a stable flight is realized