足裏の滑りを利用した２足歩行ロボットの方向転換―滑り現象のモデル化と回転角の予測―

This paper presents preliminary results and analysis on generating turning motion of a humanoid robot by slipping the feet on the ground. Humans unconsciously exploit the fact that our feet slip on the ground; such slip motion is necessary for humanoids so as to realize sophisticated human-like motions. In order to generate the slip motion, we need to predict the amount of slip. We propose the hypothesis that the turning motion is caused by the effect of minimizing the power generated by floor friction. A model of rotation by friction force is described on the basis of our hypothesis. The case that a robot applies the same force on both feet is discussed; then, we extend the discussion to the case of different force distribution. The hypothesis is verified through experiments with a humanoid robot HRP-2.

We aim to develop a humanoid robot which can go anywhere an ordinary human covers. For such purpose, we consider enhancing the walk and work capabilities of a humanoid robot by integrating manipulation and locomotion. Especially, we focus on three motions of a humanoid robot: pushing a heavy object placed on the floor, working while thrusting a hand on to a desk, and ascending large steps while holding a handrail for support. These motions are realized by experiment using the humanoid robot HRP-2.

A planning method that allows humanoid robots to pass under obstacles is developed. From experimental analyses of human motions, we find a new category of the motion: a human turns sideways and passes under an obstacle. In order to realize this kind with humanoid robot HRP- 2, statically stable and geometrically possible initial and goal configurations are analyzed. A planning method that can connect two configurations with dynamic stability is proposed. It consists of motion path and motion timing planner. Motion path planner can generate statically stable under considering joint movable range and collision avoidance. Motion timing planner can generate dynamically stable under considering ZMP conditions and maximum joint velocities. The effectiveness of the proposed method is experimentally confirmed with humanoid robot HRP-2.

This paper deals with the problem of static and dynamic (or kinetic) friction, namely the coefficients of friction for the two states. The coefficient of static friction is well known, and its theory and practice are commonly accepted by the academia and the industry. The coefficient of kinetic friction, however, has not fully been understood. The popular theory for the kinetic friction is that the coefficient of dynamic friction is smaller than the coefficient of static friction, by comparison of the forces applied in the two states. After studying the characteristics of the coefficient of friction, it is found that the comparison is not appropriate, because the inertial force was excluded. The new discovery in the paper is that coefficients of static friction and dynamic friction are identical. Wheel “locked” in wheel braking is further used to prove the conclusion. The key to cause confusions between the two coefficients of friction is the inertial force. In the measurement of the coefficient of static friction, the inertial force is initiated as soon as the testing object starts to move. Therefore, there are two forces acting against the movement of the object, the frictional force and the inertial force. But in the measurement of the coefficient of kinetic friction, no inertial force is involved because velocity must be kept constant.

This paper proposes a unified optimization framework to solve the time-parameterization problem of humanoid-robot paths. Even though the time-parameterization problem is well known in robotics, the application to humanoid robots has not been addressed. This is because of the complexity of the kinematical structure as well as the dynamical motion equation. The main contribution of this paper is to show that the time parameterization of a statically stable path to be transformed into a dynamically stable trajectory within the humanoid-robot capacities can be expressed as an optimization problem. Furthermore, we propose an efficient method to solve the obtained optimization problem. The proposed method has been successfully validated on the humanoid robot HRP-2 by conducting several experiments. These results have revealed the effectiveness and the robustness of the proposed method.

Lowering friction coefficient under low loads by minimizing effects of adhesion force and viscous resistance

A motion planning method for humanoid robots to pass under obstacles is proposed. The proposed motion planner can calculate a goal configuration and connect it with an initial configuration in a collision-free dynamically stable motion. The method can generate not only a body motion but also the footstep sequence. The effectiveness of the proposed method was validated by experiments with the humanoid robot HRP-2.

Humanoid robots being underactuated, they need to interact with their environment to move. When optimizing trajectories, the contact forces must therefore be taken into account. In this paper, we first highlight an issue encountered when using parametrized functions in the presence of equality constraints which need to be satisfied continuously over a time interval. We then propose a parametrization of contact forces and a formulation of the constraints which allow to write a tractable optimization program whose solution verifies at any instant all the constraints where those forces appear. We finish by exemplifying the approach with preliminary results obtained on a HRP-2 humanoid robot.

To achieve the complete car driving task with a humanoid robot, it is necessary to develop a set of basic action primitives, including: walking to the vehicle, manually controlling its commands (ignition, accelerator and steering), and moving with the whole-body, for car ingress/egress. In this paper, we propose an approach for realizing the central part of the complete task, consisting in driving the car along a road. The proposed method is composed of two main parts. First, a vision-based controller uses image features of the road, to provide the reference angle for the steering wheel. Second, an admittance controller allows the humanoid to safely rotate the steering wheel with its hands and realize the desired steering command. We present results from a car driving experience, by humanoid robot HRP-4, within a video game setup.

This paper addresses the problem of generating dynamic motion for a humanoid between two predefined postures. The humanoid robot starts its motion from a statically stable configuration to another known statically stable configuration when it is necessary to make step; that is, the center of mass (COM) is inside the support polygon at the initial and the goal configurations. A dynamic motion generation method is proposed in order to generate the whole-body robot motion. Zero moment point preview control is used for maintaining dynamic stability while the robot is taking the step and suitable splines are used for generating the hands, neck and foot motion because it is a free motion on the field of gravity; the screw theory is applied under Lie groups for kinematics and dynamics humanoid modeling, which allows smooth and natural humanoid motion. Successful results on the HRP-2 humanoid robot are shown and discussed.

Researches in the brain science field have uncovered the human capability to use tools as if they are part of the human bodies (known as tool-body assimilation) through trial and experience. This paper presents a method to apply a robot's active sensing experience to create the tool-body assimilation model. The model is composed of a feature extraction module, dynamics learning module, and a tool-body assimilation module. Self-organizing map (SOM) is used for the feature extraction module to extract object features from raw images. Multiple time-scales recurrent neural network (MTRNN) is used as the dynamics learning module. Parametric bias (PB) nodes are attached to the weights of MTRNN as second-order network to modulate the behavior of MTRNN based on the properties of the tool. The generalization capability of neural networks provide the model the ability to deal with unknown tools. Experiments were conducted with the humanoid robot HRP-2 using no tool, I-shaped, T-shaped, and L-shaped tools. The distribution of PB values have shown that the model has learned that the robot's dynamic properties change when holding a tool. Motion generation experiments show that the tool-body assimilation model is capable of applying to unknown tools to generate goal-oriented motions.

The search for sustainable and environmentally friendly alternatives to conventional mineral-based oil lubricants has led to increasing research into bio-based lubricants for industrial applications. This study develops biodegradable lubricating oil for turbine bearing from palm kernel and castor oils through chemical modification and additive blending. Using transesterification method, the oils were converted to their various methyl esters and blended with additives such as ethylene glycol, ascorbic acid, trimethylopropane, and graphene. Physiochemical evaluations of both palm kernel and castor developed lubricating oils showed improved viscosity, pour point, thermal stability, neutralization number, and flash point against their natural oils. While their tribological evaluation revealed that among the developed samples, Palm kernel oil-based turbine lubricant 1 (PKOTL1) exhibited the best tribological properties having the lowest friction coefficient (0.047), with less frictional force (2.617N), as against the mineral oil ISO VG6 conventionally used for lubricating turbine bearings with coefficient of friction (0.051) and frictional force (2.862N), this improved tribological properties were as a result of the additives blend in the development of the bio lubricating oil. Among the developed castor oil turbine lubricant, sample (COTL3) have the lowest coefficient of friction (0.058), less frictional force (3.242N) and low wear scar diameter (0.49mm). The study confirms that the modified vegetable oils are suitable, eco-friendly alternatives to mineral-based turbine lubricants with competitive performance with that of mineral oil ISO VG46.

This paper presents a biped locomotion control method for an exploration in unknown environment. It is still hard for humanoid robot to walk autonomously even an artificial construction such as a power plant which are overly arranged many stairs and pipes. In such an environment, the physical constraints with a collision avoidance, joint limitations and support region on the terrain, become dominant limitations for an exploration. Updating environmental information to biped walking pattern generator and controller, a biped walking on a complicated rough terrain can be achieved. The proposed method is validated through several dynamics simulations with the HRP-2 humanoid robot.

This paper is about the design of humanoid walking pattern generators to be used for physical collaboration. A particular use case is a humanoid robot helping a human to carry large and/or heavy objects. To do this, we construct a reduced model which takes into account physical interaction. This is used in a model predictive control framework to generate separate behaviors for being a follower or a leader. The approach is then validated both on simulation and on the HRP-4 humanoid robot.

Polyvinylidene Fluoride (PVDF) is a commercially available, piezoelectric polymer. It is widely utilized due to its advantageous mechanical, chemical, and electromechanical properties. In this paper, we discuss a PVDF microgripper and then characterize its gripping (frictional) force. This mechanical characterization of the PVDF will be helpful to design the microgripper. The microgripper has many applications like surgeries, microassembly and micromanipulation. The friction force is an important criterion that greatly affects the gripping. The actuation of the gripper is done by applying voltage. It is fundamentally interesting to understand the effects of the applied voltage on the coefficient of friction. The friction force was measured as a function of the applied voltage. It was found that the increasing voltage applied on the sample lead to an increase in the coefficient of friction measured on the surface of the sample. This work serves as an investigation on the effect of applied voltage on the friction coefficient of the sample. The possible mechanisms behind this phenomenon are also discussed here.© 2006 ASME