This paper firstly derives and analyzes non-linear dynamics of pinch motions generated by a pair of robot fingers (1 D.O.F. and 2 D.O.F.) with soft tips and finds a feedback control signal for stable grasping and posture control of a rigid object based on passivity analysis. It is shown that measurements of rotation angle of the object by means of optical devices play a crucial role in construction of such feedback signals. Secondly, results of computer simulation by using the derived non-linear differencial equations with geometric constraints and results of experiments by using such robot fingers are presented. Then, usefulness of this control method is discussed from the practical viewpoint.
Recently new ultrasonic flaw detection system called TOFD method is utilized to externally inspect the inner and outer surfaces of weld seams on spherical storage tanks while tanks are in use. In this system, the robot mounted the sensors is not only permitted to travel along the weld seam rapidly but also required to follow the weld seam precisely. In this paper, the development of a magnetic-wheeled robot that can follow the weld seam within 5 mm based on feedback control is described. Since October 1999, this robot has been in experimental operation on an actual spherical tank. Practical introduction of this robot will shorten inspection time by about one week from the conventional 23 days, and will reduce the conventional number of inspectors required from 4 to 2-3.
In this paper, we propose a decentralized motion control algorithm of multiple mobile robots referred to as DR Helper for transporting a single object in cooperation with a human. DR Helper consists of an omni-directional mobile base, a six-axis body force sensor, a folk lift, and an onboard controller. Each robot is controlled by its own controller as if it has a caster-like dynamics and transports a single object together with other robots based on an intentional force/moment applied by a human. The adaptive dual caster action is also proposed to improve the maneuverability of the system. Experiments using multiple DR Helpers will illustrate the validity of the proposed control algorithm.
This paper introduces an open architecture humanoid robotics platform (OpenHRP for short) on which various building blocks of humanoid robotics can be investigated. OpenHRP is a virtual humanoid robot platform with a compatible humanoid robot, and consists of a simulator of humanoid robots and motion control library for them which can also be applied to a compatible humanoid robot as it is. OpenHRP is expected to initiate the exploration of humanoid robotics on an open architecture software and hardware, thanks to the unification of the controllers and the examined consistency between the simulator and a real humanoid robot.
Power grasp or enveloping grasp enables a multifingered robot hand to grasp an object firmly by constraining the object with multiple contacts on its surfaces. The equilibrium equation in power grasp is not enough to determine the grasp force solution uniquely. It is known that if the contacts are elastic and their stiffness values are known, the grasp force solution can be computed uniquely. However, if they are not known, such computation is impossible. A more realistic assumption is that the range of the contact stiffness rather than its single value is known. Then the problem is to compute the corresponding set of grasp force solutions. This paper analyzes the solution as the function of stiffness parameters. We prove that each force component in the solution is maximum or minimum for a combination of the minimum or maximum values of the stiffness parameters. The number of such combinations is two to the n-th power where n is the number of the stiffness parameters. The ratio of the friction to normal forces at each contact is of the same function. Numerical examples show the effectiveness of the proposed algorithm.
In this paper, we present a real-time decision making method for a quadruped robot whose sensor and locomotion have large errors, considering the observational cost and the optimality. We make a State-Action Map by off-line planning considering the uncertainty of the robot's location with Dynamic Programming. Using this map, the robot can immediately decide optimal action which minimizes the time to reach a target state at any states. The number of observation, swinging its head, is also minimized by taking the time cost of observation into account. We compress this map for implementation with Vector Quantization. The total loss of optimality through compression is minimized by using the differences of the values between the optimal action and the others. In the simulation, the performance of some soccer behaviors were improved in comparison with current methods. The low computation under the restriction of the memory was verified in the experiment.
From 1970's, legged robots have attracted much attention of many researchers. In spite of this, it has been regarded that dynamically stable walking is very difficult to be tackled for any types of legged robots. For a trot gait for quadruped walking robots, we have proposed “the sway compensation trajectory”. This method utilizes a lateral, longitudinal, and vertical motion of a robot body to keep a zero moment point (ZMP) on a diagonal line between support legs. In this paper, we develop the sway compensation trajectory for a biped robot, and show that dynamically stable walking is realized. This method makes it quite easy to design stable ZMP and COG (center of gravity) trajectories, which have been regarded as a very complicated and delicate problem. The effectiveness of the proposed method is verified through computer simulations and walking experiments by a humanoid robot HOAP-1, and YANBO-3.
Visual attention is one of the most important issues for a vision guided mobile robot. Methods have been proposed for visual attention control based on information criterion   . However, the robot had to stop walking for observation and decision. This paper presents a method which enables observation and decision more efficiently and adaptively while it is walking. The method uses the expected information gain from future observations for attention control and action decision. It also proposes an image compensation method to handle the image changes due to the robot motion. Both are used to estimate observation probabilities from the observation while it is walking and then action probabilities are estimated from a decision tree based on the information criterion. The method is applied to a four legged robot. Discussions on the visual attention control in the method and the future issues are given.