Passing under Obstacles with Humanoid Robots

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.

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.

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.

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 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.

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.

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.

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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.

SummaryWith an ability to mimic the human behaviour and replace human efforts in proper platforms, humanoid robots have always acquired a special place among robotics practitioners. Being a complex method of analysis, navigation and path planning, humanoid robots still possess an interesting yet challenging area of investigation. In the current work, a novel navigational strategy has been proposed for smooth and hassle-free movement of single as well as multi-humanoid robots in complex environments. Here, the navigational plan is based on a virtual target displacement strategy which is activated when the robot is unable to find a safe path along the actual target line. After detection of a potential obstacle by the sensors of the robot, a number of virtual targets are generated around the actual target. Then, the most feasible path and point to move are calculated by assigning suitable weightage through several selected parameters to each target line and visualizing the safest path. The proposed approach is implemented on a V-REP simulation platform, and the simulation results are also validated against an experimental set-up prepared under test conditions. The validation of simulation results against experimental counterparts has revealed satisfactory agreement between them. To avoid possibility of any inter-collision during navigation of multi-humanoids under a common platform, a Petri-Net strategy has been integrated along with the proposed control strategy. Finally, the developed approach is also assessed against another existing navigational controller, and a significant performance improvement has been observed.

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.

A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

Path planning is a fundamental component of mobile robots. In case of large maps, path planning is a time consuming process, particularly for robots equipped with embedded computers and has adverse effects on the real-time response of the robots. This paper takes a fresh look at the path planning problem by considering it to be a caching or tabular-lookup problem. The robots ‘cache’ their generated paths and arm trajectories across various start and goal configurations. These cached paths are stored in a database accessible to robots in the network. Robot path planning is then reduced to a simple table-lookup which can be resolved in real-time. To overcome a cache-miss, a micro-grid and macro-grid based caching is proposed for real-time path generation for ‘nearby’ start and goal configurations. Another important characteristic of the proposed cache based path planning is that multiple robots also update the locations of the new obstacles in the map. This benefits other robots as they are able to get an updated information about the new obstacles in remote locations of the map, without explicitly discovering those obstacles by themselves. We show that by considering the robot path planning as a caching problem, robots can achieve faster and real-time responses, particularly in case of large maps with multiple robots in a sensor network.