Microwave Doppler sensors commonly used in the auto-door system have been used to navigate a rover. A collimated microwave beam with the angular width of ±3[deg] in a frequency of 24[GHz] has been emitted from the sensor:A equipped with a dish antenna of 30[cm] in diameter. Another sensor: B which works as a detector, has been loaded on the rover. The rover has been so programmed as to make right turn when the microwave signal is more than a specified level and to make left turn when the signal is less than that. Although it is conceivable that light beam is used for the navigation control, the rover can't be navigated by light beam because of its little diffraction even if the interrupting objects are small. In contrast, the method proposed in this study has the advantage of removing a certain degree of influence from interrupting objects such as irregular ground surfaces because microwaves undergo diffraction. In this paper, the base station and the rover were designed and fabricated. In addition, some experiments were performed to confirm the effectiveness of the proposed navigation control for the planetary exploration rover and to investigate the characteristics of microwave beam.

A computational framework that is extremely fast to learn without a labyrinth of coding is known as physical reservoir computing. This computation inputs some action into a real physical system and obtains an output from the observed nonlinear dynamics of the system. It has been suggested that this output can be applied to machine learning to achieve high accuracy in time series data prediction and pattern recognition. Therefore, verification of the materials and structural factors required for highly practical physical reservoir computing is underway from a materials engineering perspective. In particular, it is important to clarify how the different physical properties of materials affect the time series data extracted from the reservoir, which responds to physical actions, and how this positively impacts learning efficiency. In this study, we developed two piezoelectric sensing end-effectors, Gel Biter and Gel Toucher, which consist of two groups of 3D structured polymeric materials inspired by the human mouth and fingers. Then, we discuss the accuracy of physical reservoir computing and the necessary elemental verification in non-destructive tactile discrimination between biting and touching. As a result, we found that both achieved a tactile discrimination accuracy of over 80%, and that the integration of different nonlinear responses, in other words, a multidimensional capture, is useful for accurate object recognition by selecting and combining the reservoirs to be used to improve accuracy.

In recent years, the need for robots and IoT devices that can be operated remotely has been increasing against the backdrop of labor shortages and changes in work styles. However, when remotely operating actuators of robots and IoT devices via a communication network, there is a problem that operability deteriorates due to communication delays. This paper proposes a control method that improves operability by correcting the trajectory of actuators in response to communication delays, and reports on the effectiveness of the method through experimental verification.

Imitation Learning is a method of supervised learning using human motion data to mimic human actions. It has advantages such as high sample efficiency because it learns from successful data and can obtain tasks without program modification once the data is collected. However, most robots using imitation learning operate with positional control, and it is difficult for them to perform actions that mimic the environment in tasks involving contact and passively adapt to differences in the position and shape of objects. In contrast, bilateral control-based imitation learning, which is a teleoperation technique, has been proposed as an imitation learning method that predicts force commands and performs force control. In this study, a hamburger assembly task was performed using bilateral control-based imitation learning. By evaluating the success rate of this task, we verified the effectiveness of bilateral control-based imitation learning for tasks that require handling irregularly shaped and non-rigid objects together.

This research aims to reveal the effective interaction between motors and artificial muscles in vertical jumping, and to increase the jumping height on a humanoid robot by a hybrid drive of motors and artificial muscles. This paper describes the effect of artificial muscle drive on motors in a humanoid robot with a hybrid system of motors and artificial muscles. From the results in this study, it is expected that the artificial muscle drive can reduce the required torque of motor and contribute to an increase in vertical jumping height by setting a faster target trajectory of the joint angles.

State estimation is one of the key techniques to control soft robots, which is difficult to use sensors like traditional robots. Though modeling is an important process to achieve this, that of soft robots is challenging because of their highly nonlinear and uncertain behaviors. Many models were proposed, and we also proposed the one which contained stochastic distributed parameters for their uncertainty. In this study, we propose the state estimation method of a soft finger by using Kalman Filter framework. Stochastic parameters are suitable for constructing an appropriate estimator. Through an experimental validation, the performance of proposed estimator is discussed.

Tracked vehicles has been widely used in unstructured environments such as on uneven/deformable terrains. The mobility of the track unit consisting of interconnected metal plates is the key to enhance the safety of the vehicle. This study focuses on the track and soil interaction, particularly the wetted soil. The Distinct Element Method is applied for analyzing the interaction stress between the track and soil with varied moisture content and slip ratio. The analysis revealed that the significant stress is generated to the leading edge of the track unit and the stress distribution shifts toward the rear region of the track in moist soil.

This paper studies a measurement index that evaluates manipulation security attributed to applied forces, torques, and geometric constraints. In robotic manipulation, target objects are often constrained with force equilibrium as force closure. Otherwise, caging grasps with geometric confinement can be considered. Both strategies can practically contribute to secure manipulation, though only either index is often validated due to different dimensions. This paper proposes a homogeneous evaluation of the two assessments based on potential energy calculated with mechanical analysis. The target object has difficulty reaching an arbitrary point in the configuration space because of interferences by applied forces and obstacles. Assuming that the obstacles never move even when infinite forces are applied, the target object with infinite energy cannot reach the point beyond the obstacles.