Safe motion planning computation for databasing balanced movement of humanoid robots

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.

Research in robotics is growing and many robots are being produced. Robots are remarkable media because they can help humans and they can act in the real world. So, it is important to make the control of robots as easy as possible. For this reason we have developed the script language for humanoid robots called MPML for humanoid robots or MPML-HR which is simple enough to allow everyone to make multimodal presentation contents using the humanoid robot. Further, we evaluated the humanoid robot's presentation ability to find the difference in audience impression between the humanoid robot and the 2-D character agent. Psychological evaluation was used to compare the impression of the humanoid robot's presentation with the 2-D character agent's presentation. Two methods were used for psychological evaluation and the efficiency of the humanoid robot's presentation was verified

Teaching human motion to a humanoid social robot is important because it enables a humanoid social robot to interact with humans more naturally and in a friendly manner. Recently, a depth camera-based human motion teaching has been developed. However, inverse kinematic analysis using human motion skeletal data obtained from the depth camera is not general but rather robot structure-dependent. Thus, in this paper, we present a more general approach to compute joint angles from the skeletal data obtained from a depth camera using a recursive inverse kinematic algorithm. With a known base body orientation, each joint angle is recursively computed from the base body (torso) to the tree end body (robot hand). To validate the proposed recursive inverse kinematic algorithm, simulations of a humanoid robot model have been carried out using the RecurDyn multibody analysis software. The virtual humanoid robot consists of two 6 DOF arms and a 2DOF waist. The human upper body motion when walking has been captured using a Microsoft Azure Kinect depth camera. The hand motion from the depth camera and that from the simulation have been compared to investigate the effectiveness of the proposed algorithm.

While recent advances in approaches for control of humanoid robot systems show highly promising results, consideration of fully integrated humanoid systems for solving complex tasks such as disaster response has only gained focus recently. In this work, a software framework for humanoid disaster response robots is introduced. It provides newcomers as well as experienced researchers in humanoid robotics a comprehensive system comprising open source packages for locomotion, manipulation, perception, world modeling, behavior control, and operator interaction. The system uses the Robot Operating System (ROS) as a middleware, which has emerged as a de-facto standard in robotics research in recent years. The described architecture and components allow for flexible interaction between operator(s) and robot from teleoperation to remotely supervised autonomous operation while considering bandwidth constraints. The components are self-­contained and can be used either in combination with others or standalone. They have been developed and evaluated during participation in the DARPA Robotics Challenge and their use for different tasks and parts of this competition are described.

This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms, since the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two key concepts are adopted in this work: First, the Gaussian process latent variable model (GP-LVM) is utilized for low-dimensional representation of the original configuration space. Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot. Utilizing the GP-LVM and the duality between control and estimation, we construct a fully probabilistic generative model with which a high-dimensional motion planning problem is transformed into a tractable inference problem. Finally, we compute the motion trajectory via an approximate inference algorithm based on a variant of the particle filter. The resulting motions can be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

Problem statement: Present researches focus to make humanoid robots more and more autonomous so they can assist human in daily works like taking care of children, aged or disabled persons. In such social activities, the contemporar y humanoid robots are expected to have human like morphology and gait. Studies on bipedal locomotion for humanoid robots are then part of the hottest topics in the field of robotic researches. Knowing the benefits of arm swinging for human gait, we propose in this study a new prototype of female humanoid robot morphology having the capabilities to swing arms during step walking. Approach: A new humanoid robot prototype had been introduced based on a human morphology corresponding to a woman whose weight is 70 kg and height is 1,73 m and using realistic gait parameters of a women. The female hu manoid robot prototype was composed of fifteen links associated to twenty-six degrees of freedom. Winter statistical model had been applied to determ ine all physical parameters corresponding to each link. Modeling the proposed humanoid robot implies first to establish the kinematic model basically founded on Euler's transformation matrix and then to set th e dynamic model computed using the Newton-Euler method. To show how the arms played an important role in bipedal gait, we had chosen to consider the whole body as two independent robotic systems: the upper body and the lower body. Results: Both three dimensional kinematic and dynamic models of the humanoid robot had been developed. The three dimensional humanoid robot was controlled via a feedback linearization control during the single su pport, impact and double support phases. The simulation results showed the arm swing during the step of walking. Conclusion: The humanoid robot proposed has a human like morphology and ensures the function of a step walking with arm swinging. The applied control laws have ensured to the robot desired performances during a step walking.

This paper presents a component-based framework of humanoid robot task coordination using Predicate Transition Nets (PrT Nets). Humanoid robots have a large number of degrees of freedom (DOF) and they are expected to generate human-like stable behaviors to finish missions. Their motions have to satisfy a set of constraints (balance, coordinated motions, collision free movements), which increases the complexity of the system - each movement may consist of several types of movements and tasks simultaneously. Task is defined as a sequence of movements and/or actions towards to completing a given mission. It is key to represent the task coordination precisely and correctly to program on humanoid robots' motions. To solve the challenge issue of multiple task coordination for a humanoid robot, we presented a framework that integrates Component Based Software Development (CBSD) with Predicate Transition Nets to analyze the correctness of task co ordinations. Each component's behavior can be represented by a Predicate Transition Net, and the constraints of the behaviors are described by a set of temporal logic formulae. To ensure the task co ordinations are met, a rewriting logic-based model checker is applied to verify the system against the constraints. This formal framework is general and can be used for other type humanoid robots. It allows the representation of motion plans and provides the flexibility of semantic analysis on the humanoid robotics systems.

This paper describes integrated/intelligent humanoid robot system for daily-life environment tasks. We have realized complex behaviors of a humanoid robot in daily-life environment based on motion planner technique using an environment and manipulation knowledge. However in order to adapt to unknown or dynamic situations, sensor based behavior variation is essentially important. In this paper, we present a design and implementation of sensor based behavior verification system using an environment and manipulation knowledge, which is also used in manipulation motion planner. We also present software architecture that allows us to write a single stream code to perform complex concurrent humanoid motions. By using this architecture, sensor based verification functions are easily integrated in motion generation functions. Finally, we demonstrated a water-pouring task and a dishwashing task of the life-sized humanoid robot HRP2-JSK in a real environment while verifying its own motion.

A central challenging problem in humanoid robotics is to plan and execute dynamic tasks in dynamic environments. Given that the environment is known, sampling-based online motion planners are best suited for handling changing environments. However, without learning strategies, each task still has to be planned from scratch, preventing these algorithms from getting closer to realtime performance. This paper proposes a novel learning-based motion planning algorithm for addressing this issue. Our algorithm, called the attractor guided planner (AGP), extends existing motion planners in two simple but important ways. First, it extracts significant attractor points from successful paths in order to reuse them as guiding landmarks during the planning of new similar tasks. Second, it relies on a task comparison metric for deciding when previous solutions should be reused for guiding the planning of new tasks. The task comparison metric takes into account the task specification and as well environment features which are relevant to the query. Several experiments are presented with different humanoid reaching examples in the presence of randomly moving obstacles. Our results show that the AGP greatly improves both the planning time and solution quality, when comparing to traditional sampling-based motion planners.

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.

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.

A robot system that can process environmental measurements and motion planning during locomotion is necessary to continuously perform various tasks. To achieve such a system, which we call the Perception-during-Traversing Model, the accuracy of environmental recognition must be improved and computational costs must be reduced; these are tradeoff relationships. In this paper, we propose a construction framework for a humanoid robot to solve the trade-off problems and achieve the Perception-during-Traversing Model system. The key idea of the proposed framework is subdividing and re-integrating the localization and recognition processes in a complementary manner based on task-oriented frequency and accuracy requirements. Moreover, we apply our framework to the humanoid robot JAXON, and demonstrate that it can execute various tasks continuously by the Perception-during-Traversing Model. The most important contribution of our framework is enabling the humanoid robot to localize itself accurately and measure the environment densely enough to execute tasks using its on-board computers; this provides a practical solution to the trade-off between recognition quality and computational costs.

The gleno-humeral (GH) rotation centre is typically estimated using predictive or functional methods, however these methods may lead to location errors. This study aimed at determining a location error threshold above which statistically significant changes in the values of kinematic and kinetic GH parameters occur. The secondary aims were to quantify the effects of the direction of mislocation (X, Y or Z axis) of the GH rotation centre on GH kinematic and kinetic parameters.Shoulder flexion and abduction movements of 11 healthy volunteers were recorded using a standard motion capture system (Vicon, Oxford Metrics Ltd, Oxford, UK), then GH kinematic and kinetic parameters were computed. The true position of the GH rotation centre was determined using a low dose x-ray scanner (EOS™ imaging, France) and this position was transferred to the motion data. GH angles and moments were re-computed for each position of the GH rotation centre after errors of up to ± 20 mm were added in increments of ± 5 mm to each axis. The three-dimensional error range was 5 mm to 34.65 mm.GH joint angle and moment values were significantly altered from 10 mm of three-dimensional error, and from 5 mm of error on individual axes. However, errors on the longitudinal and antero-posterior axes only caused very small alterations of GH joint angle and moment values respectively. Future research should develop methods of GH rotation centre estimation that produce three-dimensional location errors of less than 10 mm to reduce error propagation on GH kinematics and kinetics.

Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.