This paper proposes an actuation mechanism of robotic arms using rotational counterweights. The robotic arm is driven by gravitational and inertia forces of the counterweight mounted on links. The joints of the robotic arms are able to rotate freely though this robot does not have any actuators on joints. Thereby, the robot has high joint flexibility. Additionally, the counterweight system reduces the moment applied to base link caused by gravity effect since the posture of the arm keeps a gravitational equilibrium state statically. Therefore, it is expected that the robotic arm is utilized in narrow and high altitude environment or on small vehicles. This paper also proposes a control method of the new robotic arm. The control method realizes precise position control of joint angle. The usefulness of the method is demonstrated by numerical simulations and experiments using a prototype.

This paper presents adaptive tracking control with a partial regressor for multi-joint robots. The proposed controller can abbreviate the elaborate computations of a regressor matrix, which needs to be wholly calculated in conventional adaptive control. Therefore, this controller can be easily implemented in robotic systems. Even though the proposed controller does not utilize robustification techniques of adaptive control such as deadzone, σ-modification, e-modification, and projection, the trajectory tracking performance and robustness were guaranteed through analysis of the time-averaged tracking error.

This paper presents a motion control method for multi-joint robots, which involves the use of mechanical elastic elements with adjustable stiffness. Elastic elements are installed at each joint of the robots, and the proposed control method adaptively adjusts the stiffness of the elements. This stiffness adaptation minimizes actuator torque, while the control method generates fixed periodic motions. Advantages of the control method are that it works well without requiring the knowledge of the exact values of physical parameters or elaborate numerical calculations. We mathematically show trajectory-tracking performance of the control system, and the effect of torque minimization. Experimental results demonstrate that the proposed control method greatly reduces actuator torque while generating the desired motions.

This paper presents a novel method for controlling a single-joint robot arm driven by two pneumatic artificial muscles (PAMs). We introduce the concepts of the agonist-antagonist muscle-pairs ratio (A-A ratio) and the agonist-antagonist muscle-pairs activity (A-A activity), and demonstrate that our concepts enable separate linear control of the equilibrium joint angle and joint stiffness. We also discuss our approach in comparison with the equilibrium-point (EP) hypothesis.

This paper presents an electromyographic-based human-machine interface for the agonist-antagonist system with two pairs of pneumatic artificial muscles (PAMs) that replicates the human elbow-joint system. We introduce the novel concepts of agonist-antagonist muscle-pair ratio (A-A ratio) and agonist-antagonist muscle-pair activity (A-A activity) to link the human muscle system to the PAM system, and we propose a linear control method translating the equilibrium point of human muscle system into that of PAM system. The human-robot experiment demonstrates the validity of the proposed method.

This paper proposes an energy efficient method for pick and place tasks of SCARA robots. In the proposed method, an adaptive elastic device at each joint of a robot is effectively utilized to reduce the total energy of pick and place tasks. For practical pick and place tasks, start/end points must be changed depending on the required task. In the previous works related to adaptive elastic device for energy saving, it is not clear how to change the start/end points. In this paper, a method to change positions of start/end points of pick and place tasks is proposed. The energy efficient performance of the method is demonstrated by the experiments, in which a SCARA robot with an air cylinder and a vacuum pad is used for the pick and place task of chocolate plates.

Estimation of joint torque is an important objective in the analyses of human motion. In particular, many applications seek to discern torque during a desired human motion, which is equivalent to solving the inverse dynamics. The computed torque method is a conventional means of calculating inverse dynamics. The obtained torque, however, invariably includes errors resulting from inexact inertial and viscoelastic parameters. This paper presents a method for solving the inverse dynamics of a human arm passively during tracking. To achieve precise human motion tracking, iterative learning control is used for motion generation. Some experiments that target a human arm are executed to validate the proposed method.

This paper proposes a task space trajectory tracking controller based on resonance for multi-joint robots. This controller generates desired motions, which are specified in the task space, while adjusting stiffness of mechanical elastic elements installed in each joint of the robots. This controller also adjusts equilibrium angles of the elastic elements. These parameter adjustments minimize actuator torque. Advantages of the proposed controller are to work without using exact parameter values of the controlled systems nor huge numerical calculations. We mathematically discuss stability of the controlled systems. Simulation results demonstrate the effectiveness of the proposed controller.

This paper proposes passivity-based controllers for periodic motions of multi-joint robots with impact phenomena. Even the robot motions with impact phenomena have complex dynamics, we try to analyze stability of the controlled systems by adopting some assumptions and using Lyapunov-like functions. At first, we present a passivity-based feedback controller. Secondly, we present a repetitive controller based on passivity-based iterative learning control. These controllers generate desired periodic motions, which are specified by users of the controllers. Advantages of the proposed controllers are to works well without using exact parameter values of the controlled systems nor huge numerical calculations.

In this paper, we propose a new mechanical structure for adjustable stiffness devices with lightweight and small size. The proposed structure utilize a ball screw mechanism to adjust a relationship between infinitesimal displacements of joint rotation and a linear spring. Then, stiffness around the joint is adjusted. Unlike many of other adjustable stiffness structures, available elastic energy of the elastic element is maximum when the stiffness of the proposed structure is maximum. Therefore, the elastic element of this structure can be smaller and more lightweight than the other structures. Another advantage of the proposed structure is to require fewer and smaller mechanical parts, because the proposed mechanism mostly requires the ball screw mechanism and the linear spring. We developed an actual hardware to test the proposed structure.

This paper proposes a new trajectory tracking control method for multi-joint robots by combining stiffness adaptation and iterative learning control. The proposed controller achieves trajectory tracking while optimizing stiffness of elastic elements installed in each joint of the robots. Even though the multi-joint robots have nonlinear dynamics and multi degree-of-freedom, the stiffness optimization realizes high energy efficiency as if we utilized resonance. An advantage of the proposed control is to work well without using exact parameter values of the robots. Since it seems that adaptive control and iterative learning control have not been used simultaneously, this paper newly proposes a methodology to appropriately combine stiffness adaptation and iterative learning control. This combination enables trajectory tracking and convergence of the stiffness to the optimal one. These properties can not be achieved by our previous controllers.

In this paper, we apply an energy saving control method to a simulation model that includes some dynamics of biped walking robots. This control method was proposed by the authors, and is based on resonance of multi-joint robots. An advantage of the control method is to work well without using exact parameter values of the controlled systems nor huge numerical calculations. This paper discusses how to apply the proposed control method to walking motions as a first step to realize energy saving biped robots. For this purpose, we consider some dynamics of the biped robots. Simulation results showed that the proposed control method can generate energy saving walking motions.

This paper proposes an energy saving control method of periodic motions composed of multi-frequency components based on adaptive stiffness optimization for mechanical systems. In the case of sinusoidal motions, the concept of resonance can be easily applied to determine an optimal stiffness. This paper tries to extend resonance to deal with periodic motions composed of multi-frequency components. For this purpose, we define a cost function, which represents an amount of actuator torque. Next, an optimal stiffness is defined as the stiffness that minimizes the cost function. The relationship between the optimal stiffness and the desired motions is analytically derived. Based on the preparations, we propose two types of controller. These controllers generate the desired motions using the least amount of actuator torque as possible by optimizing the stiffness adaptively. The controllers work well without using parameters of the objective systems. Stability and effectiveness of the stiffness optimization are proved mathematically. Simulation results demonstrate the effectiveness of the proposed method.

This paper proposes a tracking control method of sinusoidal motions utilizing stiffness adjustment of mechanical elastic elements for serial link systems. Although dynamics of the controlled objects is nonlinear, the stiffness adjustment realize a condition similar to a resonance of linear systems. We present a controller that adjusts stiffness of the elastic elements to reduce torque requirement of actuators while generating desired motions. The proposed controller works without using dynamics models nor parameters of the controlled objects. Stability of the controller is proved, and tracking errors are guaranteed to converge to a certain region. Simulation results demonstrate the validity of the proposed method. We also present an application of the proposed method to power assist systems.

In this paper, we propose a power assist system that amplifies sinusoidal human's torque and attains minimization of control input requirement using an impedance control and resonance. This impedance control is designed to realize the following features: 1) sinusoidal torque amplification. 2) Minimization of control input requirement by adjusting stiffness. 3) No requirement for the knowledge of amplitude, frequency and phase of human's torque. 4) No requirement for myoelectric signals. 5) Satisfaction of causality. Convergence of the proposed controller is proven theoretically. Simulation results verify the validity of the control scheme including some conditions not discussed in the theory. Experimental results show the validity of the control system including real human's complex dynamics and feedback loops

This paper proposes power assist controllers that realize control input minimization using resonance of passive elements. To satisfy the objective, two types of systems are considered and controllers are proposed for each system. These controllers require no biosignals and satisfy causal manner. Convergence of the controllers is discussed theoretically. Simulation results show the validity of the proposed controllers. Experimental results show effectiveness of the proposed controller when the systems are used by a human operator

In this paper, a Virtual Power Limiter System is proposed. This requires no modeling and makes it possible to guarantee the stability of control systems which include unknown characteristics: flexibility in flexible manipulators, compressibility in pneumatic servo systems, human dynamics in man-machine systems, hardware nonlinearity in mechatronic systems, and so on. The details of the proposed virtual power limiter system and experimental verification with a man-machine system are presented.