Walking pattern generators designed for physical collaboration

Walking pattern generation has been a crucial issue for humanoid robot. To generate more natural walking patterns, and determine parameters more reasonably, this paper presents a method for humanoid walking pattern generation based on human walking characteristics. The characteristics of human walking parameters were obtained by capturing and analyzing human walking data. Walking pattern parameters are determined by using these characteristics, and then stable and harmonious walking patterns are generated. The effectiveness of the method was verified by experiment result on humanoid robot BHR-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.

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.

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 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.

This paper focuses on real-time walking pattern generation for humanoid robots with linear inverted pendulum model (LIPM). In general, there are many issues in generating proper walking patterns of center of mass and zero moment point (ZMP) with the LIPM since the LIPM has two drawbacks such as instability and non-minimum phase property. For resolving these difficulties, the paper proposes a new real-time approach by combining a feedback and a feedforward controller. The feedback controller employs a pole placement method which shifts the poles of the LIPM in order to improve system stability. The feedforward controller utilizes advanced pole-zero cancelation by series approximation method (APZCSA) for reducing non-minimum phase property which occurs by an unstable zero and is not able to be dealt with by the feedback controller. In addition, the APZCSA improves the tracking error induced by finite series approximation. Using the two controllers, the proposed method makes the transfer function of overall walking pattern generation system approximately unity and consequently generates a stable walking pattern which follows a desired ZMP according to walking path. The efficiency of the proposed method is verified by walking pattern planning examples and experiments with the humanoid robot MAHRU-R.

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.

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.

Purpose Walking on inclined ground is an important ability for humanoid robots. In general, conventional strategies for walking on slopes lack technical analysis in, first, the waist posture with respect to actual robot and, second, the landing impact, which weakens the walking stability. The purpose of this paper is to propose a generic method for walking pattern generation considering these issues with the aim of enabling humanoid robot to walk dynamically on a slope. Design/methodology/approach First, a virtual ground method (VGM) is proposed to give a continuous and intuitive zero-moment point (ZMP) on slopes. Then, the dynamic motion equations are derived based on 2D and 3D models, respectively, by using VGM. Furthermore, the waist posture with respect to the actual robot is analyzed. Finally, a reformative linear inverted pendulum (LIP) named the asymmetric linear inverted pendulum (ALIP) is proposed to achieve stable and dynamical walking in any direction on a slope with lower landing impact. Findings Simulations and experiments are carried out using the DRC-XT humanoid robot platform with the aim of verifying the validity and feasibility of these new methods. ALIP with consideration of waist posture is practical in extending the ability of walking on slopes for humanoid robots. Originality/value A generic method called ALIP for humanoid robots walking on slopes is proposed. ALIP is based on LIP and several changes, including model analysis, motion equations and ZMP functions, are discussed.

Mobile manipulation control method for humanoid robots provides good manipulability and stability on manipulation tasks. This method leads whole body motion and locomotion, when the manipulator tip trajectory is decided. For the dexterous and powerful manipulation, the robot has to cope with the expected and unexpected forces acting to the hands. By assuming the whole body balance, we define the “ZMP Based Reference Center of Mass (ZBC) ”, which is projection of the Center of Mass (COM) to the ground of world coordinate frame, when the external forces act to the end-effectors. We propose to use the ZBC for modification control of COM with balancing control. The method is implemented to the conventional mobile manipulation control method and we construct the system which consists of four controllers: “Foothold determination”, “Walking pattern generator”, “COM modification controller” and “Momentum controller”. The experimental results show that a humanoid robot can push a heavy object by changing the foot stamps and the COM position.

This paper is the continuation of our previous work in intercontinental, collaborative teleoperation with a humanoid robot. Our new achievement consists in an extension of the former single-arm bilateral teleoperation setting to include bimanual manipulation and walking. A coupling scheme for simultaneous manipulation and locomotion is developed. Furthermore, a task-based control framework, including a force-based control for the arms as well as a walking pattern generation, is presented to realize stable whole-body motions of the highly redundant humanoid robot. Experiments have been performed to assess the proposed control scheme. They bring to light additional scientific challenges that remain in order to reach a smooth and natural telepresent collaboration.

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.

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.

This paper presents preliminary results and analysis on generating turning motion of a humanoid robot by slipping the feet on the ground. Humans exploit the fact that our feet slip on the ground, so this is necessary for humanoids to realize sophisticated human-like motions. 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 based on our hypothesis. The proposed model suggests that the only the trajectory and the shape of the robot’s feet decide the amount of rotation by slip, and that the friction coefficient between the floor and the robot’s soles and the feet velocity do not affect the resultant angle. The case that the robot applies same force on both feet is discussed, and then we extend the discussion to the case of different force distribution. Verification is conducted through experiment with the humanoid robot HRP-2.