Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.

Using a rehabilitation robot to activate motion intention and reflex response simultaneously is an effective approach to aiding recovery from paralysis caused by neurological disorders. Mechanical motions supported by conventional robots are, however, not enough to activate reflex. In this paper, we propose a grasp-training robot that can stimulate the grasp reflex of a paralyzed hand by pushing the hand onto an elastic bar while supporting the grasping movements. In addition to this feature, we discuss the robot design in relation to its usability and wearability for ease of use in clinical practice. Experimental results obtained from healthy subjects show that the proposed robot can support grasping in a way similar to the traditional range-of-motion exercise used by therapists for grasp rehabilitation. Combining this appropriate grasping-motion support and the mechanism for pushing the hand onto an elastic bar succeeds in activating the grasp reflex of a completely paralyzed patient in a clinical test that involves monitoring electromyography signals from the paralyzed hand.

This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with co-adaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions.

Tendon-Driven Manipulators (TDM) have been used in various applications requiring low inertia robots having multiple degrees-of-freedom (DOF) and redundancy. TDMs are generally actuated by gear electric motors placed at the base of the robot, and consequently require complex cable tension feedback from force sensors or dynamic modelling to maintain cables under tension. This paper presents a high performance TDM actuated by magneto-rheological clutches along with a specialized motion control algorithm, termed Projected PID, that requires no tension feedback. A 2-DOF proof-of-concept TDM powered by magneto-rheological clutches is used to demonstrate overall controller performance, and a reconfigurable 2-DOF TDM powered by direct-drive electric motors is used to demonstrate the controller's ability to compensate for configuration variations and actuator failure. Experimental results also demonstrate the ability of Projected PID to control magneto-rheological cable-driven TDMs with high accuracy.

Tacit learning is the novel learning scheme based on the principle of biological control to create the appropriate behaviors adapted to the environment. Signal accumulation is the key factor for tacit learning in the process of behavior adaptation. To clarify the role of the signal accumulation in the learning process, we analyzed it dividing into the two processes depending on the control speed. The fast process is used for the behavior control and the slow process is used for the behavior adaptation to the environment. We developed the continuous-time controller for tacit learning with the integrators and showed that the signal accumulation can estimate a part of the robot model through the interactions between the robot body and the environment. This capability of tacit learning is useful to control a plant where the modeling errors and model changes are the critical problems for the stable controls. As the prominent example of the control of such plant, we experimentally verified that tacit learning can create the bipedal walking gait that pushes the ground by the support leg at the moment of losing contact with the ground.

In biological regulatory systems, all computations result from spatial and temporal combination of simple and homogeneous computational media. This computational scheme realize the adaptability to unpredictable environmental changes, which is one of the most salient features of biological regulations. To investigate the learning process behind this computational scheme, we propose a learning method that embodies the features of biological systems, termed tacit learning. We have constructed a controller based on the notion of tacit learning and applied it to the control of the 36DOF humanoid robot to create the bipedal walking adapted to the environment. Experiments on walking showed a remarkably high adaptation capability of tacit learning in terms of gait generations, power consumption and robustness.

The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.

This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the method’s performance to a similar algorithm is also presented.

This paper presents a mobile robotic system designed to perform deep soil sampling for lunar subsurface exploration in the near future. Drilling robots have to carry the excavated regolith backward because of its high density. Therefore a new scheme is proposed, to move forward under the soil by making use of reactive force caused by pushing the discharged regolith. Simple experiments demonstrate the effectiveness of the proposed method.

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

Hopping is effective mobility for exploration robot on small body. To explore small bodies, a mobile robot is required to move to target points. In case of exploration of microgravity environment by a hopping locomotion, hopping trajectories cannot be controlled after a robot hops. To move to target points, hopping direction and velocity should be controlled at takeoff. The authors proposed a, hopping robot, which consists of three masses, two linear actuators and a spring. This paper discusses how to control a hopping direction of the proposed robot in microgravity environment. When the robot hops, the friction force between the robot and the ground becomes larger. The simulation results show the controllability of a hopping direction even if there is a slip on a ground. The controllability is also confirmed by the free fall experiments.

Performance of mobility is highly dependent on the gravitational environment. Some researchers have proposed novel locomotion methods for extremely small terrestrial bodies like asteroids. Hopping is a possible method under micro-gravity. It is not proved, however, that the proposed method of locomotion is optimum for a given level of gravity. The purpose of this paper is to analyze which level of gravity is optimum for each mechanism, and which mechanism or parameter is optimum for each level of gravity. First, a classification of locomotion mechanism is described. Next, the speed of hopping and wheeled robots is compared. Finally, some simulation studies are performed to analyze the detailed mobility of wheeled robots.

This paper proposes a hopping robot that can move in a microgravity environment. The robot consists of three masses, which are connected by a spring and linear actuators. Using the spring and the linear actuators, the robot can obtain the vertical velocity and the horizontal velocity. Therefore, the robot can hop with the horizontal velocity from the stationary state. Furthermore, the robot can land without bounding and the slipping by transforming the kinetic energy to the elastic energy. Using this approach for controlling hopping and landing, the robot can move to a target in a microgravity environment. The simulation studies and the microgravity experiments shows the effectiveness of the proposed mobility.

This paper proposes a new mobility system for planetary rover using springs and linear actuators. In the microgravity environment, hopping mobility is a possible choice for enhancing a new mobility of the robot. One method to hop is to press one's own to the ground. The proposed robot consists of two masses, and can press one's own to the ground using springs. So the rover can hop from the stationary state by transforming the elastic energy to the kinetic energy using the springs and linear actuators. Furthermore the robot can land without bounding by transforming the kinetic energy to the elastic energy. The simulation studies and the ground experiments shows that the proposed rover could hop and land.

This paper proposes a four-wheel-type robot with the springs and linear actuators mechanism. In the microgravity environment, hopping mobility is a possible choice for enhancing a new mobility of the robot. The proposed robot can hop and land without bounding in the microgravity environment using the spring mechanism. Furthermore, at the moment of hopping, the wheels are pressed to the ground, so the rover can obtain the horizontal velocity by rotating the wheels. The wheels are also used for the attitude control in the air. The simulation of hopping with horizontal velocity is performed and the equation of kinematics of the rover is developed.

The design of an ultrasonic motor having 2-DOF is presented. The drive unit consists of four piezoelectric actuators arranged in a pyramid shape. Circular vibration at the apex generates a thrust when a rotor is pressed against the apex. The direction of the thrust within the tangent plane of the rotor is controlled by the displacement and phase differences among the four piezoelectric actuators. The theoretical analysis and design method of this actuator unit is presented. This actuator can rotate a spherical rotor. The orientation of the spherical rotor, which has three degree of freedom, is controlled by solving nonholonomic kinematics equations between the rotor and the drive unit. The solution is given in detail. The experimental results demonstrate that the direction and speed of the rotation of the spherical rotor are controllable.

A new type of mobility is discussed for space projects such as the MUSES-C aiming at small asteroid exploration. We propose the use of electro-magnetic levitation in order to integrate a mobility into the microgravity rover. The rover has a spherical shape and a smaller spherical shell inside. Four electromagnets are symmetrically located between the outer sphere surface and the inner sphere shell with one end of each directed to the center of the shell. With electromagnetic force of the magnets, a sphere iron ball inside the shell is controlled and levitated. When the rover lifts the ball inside with the electro-magnetic force, the rover is in return pressed down the ground by the reaction force, due to which the rover system not only gains upward momentum for floatation, but also obtains friction that enables its rolling on the ground. The prototype microgravity rover was developed and experimental results indicate effectiveness of the proposed mobility.