Aiming to improve positioning precision of stop posture (position and orientation) of an object and decrease trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experimental-oriented way. The experimental results show that these methods are effective. By discussing the characteristics of these control methods, we conclude that when there is no enough system knowledge, LCBCC is the only choice and to be used to learn system knowledge; after enough experience has been acquired, LCBOP is better than LCBCC in the view of precision.
This paper proposes a practical contact force control framework for force-controllable legged robots with redundant joints, which is applicable to automatic/semi-automatic control for mobile robots, construction robots, bipedal humanoid robots, and assisitive devices. The proposed method requires: (1) no contact forces measurements; (2) no inverse kinematics; (3) nor inverse dynamics in achieving the desired contact forces. Combined with a passivity-based redundancy resolution technique, a contact force closure is optimally solved and transformed directly into joint torques in real-time. Experimental results on a full-sized biped humanoid robot show that the proposed method can simultaneously control the multiple contact forces without generating internal forces, and can naturally interact with unknown external forces.
We have developed an autonomous robot carrying a heavy medicine container, to be used in the medicine factory. In this environment, its workspace is narrow and the robot has to negotiate between existing machines and instruments. It is also necessary for the robot to comply with strict GMP standards defined by Ministry of Health, Labour and Welfare, Japan for medicine production. The robot tows an empty container to the throw-in platform according to the instruction given by the process controller system via infrared communication. Once loaded, the container is again towed by the robot and stored at a designated position in the stowage area. The robot runs on magnetic rails autonomously and is able to tow a container weighing 200[kg], or heavier, and precisely position the container at the specified location with less than 10[mm] of error. The container is rigidly held by a specifically designed gripper on the robot during transportation to maintain precise motion and positioning of the container. Three robots have worked in a medicine factory for more than two years without causing a single interruption in the manufacturing process, achieving improved production efficiency, lowered production costs, with fewer workers attending the production line than before.
In this paper, we design a flight controller for a twin-rotor helicopter model with actuator constraints. The controller is composed of the state dependent gain-scheduled feedback control law and the reference management device which are adjusted optimally by on-line computation. We show that the control system with the proposed control law achieves higher tracking performance as compared to the system with the standard constant feedback control law through experimental results.
In acquiring a motion only from its objective by learning, large cost, such as damage from falling over, and a large number of trials are required if the motion is a complex one, such as jumping serve. Reusing the knowledge already learnt is an essential mechanism to learn such motions efficiently, like humans do. In this paper, we propose a learning method to decompose action-value functions for reusing in the framework of reinforcement learning. Avoidance actions that are assumed invariant across different tasks (e.g. avoiding to fall over) are learnt separately from primary actions that are assumed task specific, then the action-value function for the avoidance actions is reused in learning new tasks. Furthermore, we extend the method for multi-link robots to learn whole body motions. The proposed method is applied for moving tasks both in discrete and continuous planes, and is also applied for a tennis-serve and a jump tasks of a 4-link robot. We also demonstrate a issue in reusing of the similar method, Q-decomposition . The simulation results show an performance advantage of the proposed method over Q-decomposition in reusing avoidance actions.
We propose a cooperative motion generation method based on the haptic interaction between robots and human beings for the cooperation of robots with human beings. There are lot of problems in the human-robot haptic interaction, such as self-collisions, collisions with obstacles, and limitation of the range of joint movements. We had proposed the self-collision avoidance motion generation method for robot cooperation with human beings in our previous study. In this study, we focus on the range of joint movements of robots in order to deal with the environmental/task constraints and also to prevent self-collisions. By considering the range of joint movements, the robot could also avoid the limitation of the range of the joint movement, as well as self-collisions. Additionally, by appropriately setting the range of movements of the mobile base of robots, we can generate the cooperative motion in robots, which may assist in dealing with the environmental/task constraints. The proposed method is employed in a human-friendly robot referred to as “MR Helper,” and some experiments are carried out for validating the method.
This paper describes a model for a robot to appropriately control its position when it presents information to a user. This capability is indispensable since in the future many robots will be functioning in daily situations as shopkeepers presenting products to customers or museum guides presenting information to visitors. Research in psychology suggests that people adjust control their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener’s field of view and establish a joint view. We observed human-human interaction situations where people presented objects and developed a model for an information-presenting robot to appropriately adjust its position. Our model consists of four constraints for establishing O-space: (1) proximity to a listener, (2) proximity to an object, (3) listener’s field of view, and (4) presenter’s field of view. We also present an experimental evaluation of the effectiveness of our model.
In micro-teleoperation scenarios like micro-surgery or micro-assembly, not only precise motions to handle very small objects but also swift movements over a broad area are required to improve the task efficiency. In this paper, a framework to design a variable-scale teleoperation (VST) system, which enables an operator to adjust both velocity-scale and force-scale in real-time is proposed. Since the VST system is a linear time-varying system and the total system includes uncertain operator and environment dynamics, gain scheduling control and scaled H∞ control are introduced to guarantee the robust stability and robust performance of the system. In addition, a scale-dependent weighting on control objective is introduced to achieve better performance. A 1-DOF system was built and an experiment was conducted to confirm the stability and performance of the system designed by the proposed framework.
This paper presents a robust self-localization method based on template matching and Monte Carlo Localization in the RoboCup Middle-Size League. The method of this paper can be self-localized by template matching using information of field lines and a center circle instead of colored landmarks such as goals and corner poles. In RoboCup, real-time processing is crucial. The method can reduce computational costs of template matching using dynamically generated templates. The templates of the field are generated and matched from information of white lines and an electronic compass sensor in real-time. Moreover, the method can self-localize directly from the template matching. Thus, the kidnapped robot problem is not occurred. The experimental results indicate that the proposed method can self-localize robustly and in real-time under even partial occlusion. This method is suitable for the RoboCup Middle-Size League.