Unified motion planning of passing under obstacles with humanoid robots

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 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 describes the design of an integrated system for humanoid robotics that consists of three key components, 3D vision, motion planning and bipedal control. Layered system design is adopted to achieve concurrency as well as small latency. Implementation using our humanoid type robot H7 and experiments with this architecture are described. The H7 is expected to be a common test-bed in experiments and discussion for various aspects of intelligent humanoid robotics.

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.

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.

With the increase of the number of humanoid robots, the importance in developing a motion planning and synthesis tool that can easily be dealt by general users without a long training process have been growing for a wide range of applications. To this end, in this paper, we propose a novel user interface allowing whole-body motion planning and synthesis for humanoid robots. Our interface consists of two fundamental elements. The first element is a process for automatically constructing a 2D graphical interface from a variety of motion data by using a dimensionality reduction method so that all topological features of each motion and similarities among different motions are preserved. With the constructed interface, the user can synthesize a variety of whole-body motions by simply following the lines on the plane. The second element is a motion-interpolation process for the synthesized motions even if the user roughly indicates motions by the interface. These elements allow to synthesize smooth and naturally-looking whole body motions even by a general user. We developed a system composed of a tablet-type input device and a small humanoid robot for the validation of our method. Through experiments with human subjects, the effectiveness of the proposed approach was validated.

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.

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.

Presented is a new method of coordinated global path planning of a group of mobile agents. It is based on a continuum theory for motion of deformable bodies. Considered is a swarm of vehicles at rest in an initial configuration with no restrictions on the initial shape or the locations of the vehicles within that shape. We present a motion plan to move this swarm of vehicles from the initial configuration to a new configuration such that there are no collisions between any vehicles at any time instant. It is achieved via a linear map between the initial and desired final configuration such that the map is invertible at all times.

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.

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.

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.

Motion databasing is an important topic in robotics research. Humanoid robots have a large number of degrees of freedom and their motions have to satisfy a set of constraints (balance, maximal joint torque velocity and angle values). Thus motion planning cannot efficiently be done on-line. The computation of optimal motions is performed off-line to create databases that transform the problem of large computation time into a problem of large memory space. Motion planning can be seen as a Semi-Infinite Programming problem (SIP) since it involves a finite number of variables over an infinite set of constraints. Most methods solve the SIP problem by transforming it into a finite programming one using a discretization over a prescribed grid. We show that this approach is risky because it can lead to motions which may violate one or several constraints. Then we introduce our new method for planning safe motions. It uses Interval Analysis techniques in order to achieve a safe discretization of the constraints. We show how to implement this method and use it with state-of-the-art constrained optimization packages. Then, we illustrate its capabilities for planning safe motions dedicated to the HOAP-3 humanoid robot.