Time-optimal Path Parameterization for critically dynamic motions of humanoid robots

This work was supported by IT R & D program of MIC & IITA (2006-S-028-01, Development of Cooperative Network-based Humanoids Technology), Republic of Korea.

Humanoid robots are similar in dimension and degrees of freedom to humans. Compared with other types of robots, humanoid robots are more adaptable to complex living environment and various tools of humans. The combination of wheeled robot and humanoid robot is an effective way to increase the moving speed, range of motion and environmental adaptability of humanoid robots, which can accelerate the application of humanoid robots in human living environments. In this way, the use of humanoid robots in industry and services can be effectively improved. This article introduces a novel electrically-driven wheel-legged humanoid robot BHR-W with 14 degrees of freedom. It can use wheels to achieve rapid motion on structured pavement. In addition, BHR-W has two degrees of freedom of hip joint, giving it excellent ability to perform complex actions such as walking, jumping and roller skating to cross unstructured environments. BHR-W is an innovative design which enables humanoid robots to have capabilities of bipedal walking and wheel rolling. This article introduces the mechanical and system design of the robot, and designs a controller for balance control. Finally, the stability and reliability of the system and the feasibility of the balance control strategies are verified by several practical experiments.

In the near future, humanoid robots will act as the partners of human beings in daily life. Among numerous human-like competencies, motion of humanoid robots is critical for providing humans with richer interactions with such robots. Motion plays an essential role in complementing spoken communication. Moreover, the motions of humanoid robots generate nonverbal communication in various contexts. Through this nonverbal communication, humans can interact with robots not only directly but also indirectly or even unconsciously, as if the robot were simply part of the environment. Before the developments of humanoid robots, embodied conversational agents (ECAs) were introduced as virtual embodied representations of humans that communicated multi-modally with humans and there has been a great deal of research on ECA behavior. ECAs and humanoid robots share many features in terms of how they communicate with humans. Nevertheless, simply adapting knowledge gathered from current ECA studies to a humanoid robot study is insufficient for the following reasons: 1. ECA studies lack knowledge focused on nonverbal communication, which has become more important in the physical world; 2. ECA studies have focused on developing agent-centered intelligence rather than a user- centered experience; 3. ECA studies have developed logics to generate motions automatically rather than to provide designers with the practical knowledge necessary to design desirable motions. Motivated by these three arguments, we seek to pioneer a new field of motion design between robot engineering and design discipline. To bring this motion study into design discipline, we focused on human-centered experience through nonverbal communication with a humanoid robot. This paper aims to outline sharable user experience in order to help designer create desirable motions for humanoid robots in various speechless contexts.

This paper proposed a new method for humanoid robot gait generation based on several objective functions. The proposed method is based on multiobjective evolutionary algorithm. In our work, we considered two competing objective functions: MCE and MTC. Based on simulation and experimental results, we conclude: Multiobjective evolution is efficient because optimal humanoid robot gaits with completely different characteristics can be found in one simulation run. The nondominated solutions in the obtained Pareto-optimal set are well distributed and have satisfactory diversity characteristics. The optimal gaits generated by simulation gave good performance when they were tested in the real hardware of "Bonten-Maru" humanoid robot. The optimal gait reduces the energy consumption and increases the stability during the robot motion. In the future, it will be interesting to investigate if the robot can learn in real time to switch between different gaits based on the environment conditions. In uneven terrains MTC gaits will be more

In this article, the authors present a new method for humanoid robot motion planning, satisfying multiple objectives. In this method, the multiple objectives humanoid robot motion is formulated as a multiobjective optimization problem, considering each objective as a separate fitness function. Three different objectives are considered: (1) minimum energy consumption; (2) stability; and (3) walking speed. The advantage of the proposed method is that, in a single run of multiobjective evolution, generated humanoid robot motions satisfy each objective separately or multiple objectives simultaneously. Therefore, the humanoid robot can switch between different gaits based on environmental conditions. The results show that humanoid robot gaits generated by multiobjective evolution are similar to that of humans. To further verify the performance of optimal motions, they are transferred to the “Bonten-Maru” humanoid robot.

Legged mechanisms can walk and run but sometimes encounter a risk of falling. In this article, a general numerical framework of balance criteria for a single-support legged mechanism is proposed and demonstrated. Explicit forms of necessary and sufficient conditions for balancing are identified and the balanced state manifold is constructed accordingly in the extended phase space of joint angle, joint velocity, and actuation limit. Within the iteration loops for partitioned joint angle and actuation limit, a nonlinear constrained optimization problem is formulated where the dynamic models of the legged mechanism are incorporated. The necessary conditions for balancing, such as the Zero-Moment Point, positive normal reaction, friction, and the ability to end up at a final static equilibrium, are implemented along with the system parameters for generality. The sequential quadratic programming method numerically solves for the velocity extrema within the complete feasible domain to construct the balanced state manifold as a viability kernel, which is a reachable superset of all possible controller-based domains. The balanced state manifold, along with its demonstration using the proposed optimal balancing motion for minimum energy and biped walking motions in sagittal plane, shows valid features that are physically consistent. The framework can be extended to systems in three-dimension with higher complexity, both in single and double support phases, for the development and stability analysis of walking robots and humans.

Motion planning for humanoid robotic systems with many degrees of freedom is an important and still generally unsolved problem. To give the robot the ability of acting and navigating in complex environments, the motion planner has to find collision-free paths in a robust manner. The runtime of a planning algorithm is critical, since complex tasks require several planning steps where the collision detection and avoidance should be accomplished in reasonable time. In this paper we present an extension of standard sampling-based techniques using Rapidly Exploring Random Trees (RRT). We extend the free-bubble path validation algorithm from Quinlan, which can be used to guarantee the collision-free status of a C-space path between two samples. By using enlarged robot models it is possible to avoid costly distance calculations and therefore to speed up the planning process. We also present a combined approach based on lazy collision checking that brings together the advantages of fast sampling-based and exact path-validated algorithms. The proposed algorithms have been evaluated by experiments on a humanoid robot in a kitchen environment and by a comparison to a validation based on Quinlan's free bubbles approach.

In this paper, an walking pattern tuning system based on ZMP(Zero Moment Point) for a humanoid robot is proposed. Commonly, it is inevitable to handle the stage with proficient programming skill for realization of humanoid walking. The proposed walking pattern tuning system is a program tool to finely tune the walking pattern based on ZMP by adjusting intuitive parameters that ordinary user can understand easily. The walking pattern tuning system was applied to the humanoid robot of 110cm tall and the humanoid robot with the tuned walking pattern was tested.

This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot's center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.

Recently, the robot with many links and degrees of freedoms is actively produced. Generally, the robot that has many degrees of freedoms owns many redundant joints. So we make a suggestion for using redundant joints effectively. The paper proposes using redundant joint as a counter weight. We make a trajectory in momentum space, and we use Fourier Basis Algorism as the method for an optimization. This trajectory generating method has an effect on reducing a driving energy for throwing motion.

Learning-based algorithms remain a challenge in the field of humanoid robot motion control. Due to the diverse range of humanoid robot motions and the complexity of control, existing motion control methods often require the development of distinct control frameworks or using different network architectures. This paper proposes a keyframe-driven whole-body motion control framework for humanoid robots that allows the robot to perform a variety of motions through manually designed actions or video demonstrations. We introduce a humanoid robot motion classification method to enable the robot to learn various types of action. Moreover, we design a Dual Actor Single Critic Network(DASCN) network for whole-body control of the humanoid robot. Experimental results demonstrate that our method enables the humanoid robot H1 to execute a range of movements based solely on pre-designed motions or demonstrated videos.

When a human needs to generate a large force, the human will try to apply an impulsive force cooperating whole body. However, exerting impulsive force is one of the most difficult tasks for humanoid robots, because a reaction force caused by the applied impulsive force may bring the humanoid robot down. This paper discusses a humanoid robot motion to generate a large force utilizing an impact. Breaking wooden boards by Karate-chop is taken as a case study because it is a typical example of tasks that utilize impulsive force. A humanoid robot motion for the Karate-chop is carefully designed to maximize the speed of a hand keeping the stability. In order to validate the designed motion, experiments are carried out using a small humanoid robot Fujitsu HOAP-2. In the experiments, three kinds of wooden boards are tested to break by the Karate-chop. The strength of wooden boards is estimated striking the boards with a free-falling plummet. Stabilizing control is also applied to the robot in order to stabilize the body after the impact. The experimentation results are reported in the paper

Humanoid robot is the most intimate robot platform suitable for human interaction and services. Biped walking is its basic locomotion method, which is performed with combination of joint actuator's rotations in the lower extremity. The present work employs humanoid robot simulator and numerical optimization method to generate optimal joint trajectories for biped walking. The simulator is developed with Matlab based on the robot structure constructed with the Denavit-Hartenberg (DH) convention. Particle swarm optimization method minimizes the cost function for biped walking associated with performance index such as altitude trajectory of clearance foot and stability index concerning zero moment point (ZMP) trajectory. In this paper, instead of checking whether ZMP's position is inside the stable region or not, reference ZMP trajectory is approximately configured with feature points by which piece-wise linear trajectory can be drawn, and difference of reference ZMP and actual one at each sampling time is added to the cost function. The optimized joint trajectories realize three phases of stable gait including initial, periodic, and final steps. For validation of the proposed approach, a small-sized humanoid robot named DARwIn-OP is commanded to walk with the optimized joint trajectories, and the walking result is successful.

When users control robots with low degrees of freedom such as cleaning robot or wheel robot, a game pad and joy-stick are often used. In contrast, when users control humanoid robots, they often use the pre-set behavior patterns and operate them by playing the patterns because humanoid robots have many degrees of freedom and have difficulty in being controlled by such controllers. The pre-set postures are generally determined one by one. The process is not suitable for tele-operation as it is time-consuming and cannot be real-time. In this study, we pay attention to marionette that can achieve complicated operation by the finger-tip motion, and investigate the usefulness of marionette-like operation of humanoid robot using globe type interface.

In this thesis, we present a dynamical systems approach to adaptive controllers for locomotion control. The approach is based on a rigorous mathematical framework using nonlinear dynamical systems and is inspired by theories of self-organization. Nonlinear dynamical systems such as coupled oscillators are an interesting approach for the on-line generation of trajectories for robots with many degrees of freedom (e.g. legged locomotion). However, designing a nonlinear dynamical system to satisfy a given specification and goal is not an easy task, and, hitherto no methodology exists to approach this problem in a unified way. Nature presents us with satisfactory solutions for the coordination of many degrees of freedom. One central feature observed in biological subjects is the ability of the neural systems to exploit natural dynamics of the body to achieve efficient locomotion. In order to be able to exploit the body properties, adaptive mechanisms must be at work. Recent work has pointed out the importance of the mechanical system for efficient locomotion. Even more interestingly, such well suited mechanical systems do not need complicated control. Yet, in robotics, in most approaches, adaptive mechanisms are either missing or they are not based on a rigorous framework, i.e. they are based on heuristics and ad-hoc approaches. Over the last three decades there has been enormous progress in describing movement coordination with the help of Synergetic approaches. This has led to the formulation of a theoretical framework: the theory of dynamic patterns. This framework is mathematically rigorous and at the same time fully operational. However, it does not provide any guidelines for synthetic approaches as needed for the engineering of robots with many degrees of freedom, nor does it directly help to explain adaptive systems. We will show how we can extend the theoretical framework to build adaptive systems. For this purpose, we propose the use of multi-scale dynamical systems. The basic idea behind multi-scale dynamical systems is that a given dynamical system gets extended by additional slow dynamics of its parameters, i.e. some of the parameters become state variables. The advantages of the framework of multi-scale dynamical systems for adaptive controllers are 1) fully dynamic description, 2) no separation of learning algorithm and learning substrate, 3) no separation of learning trials or time windows, 4) mathematically rigorous, 5) low dimensional systems. However, in order to fully exploit the framework important questions have to be solved. Most importantly, methodologies for designing the feedback loops have to be found and important theoretical questions about stability and convergence properties of the devised systems have to be answered. In order to tackle this challenge, we first introduce an engineering view on designing nonlinear dynamical systems and especially oscillators. We will highlight the important differences and freedom that this engineering view introduces as opposed to a modeling one. We then apply this approach by first proposing a very simple adaptive toy-system, consisting of a dynamical system coupled to a spring-mass system. Due to its spring-mass dynamics, this system contains clear natural dynamics in the form of resonant frequencies. We propose a prototype adaptive multi-scale system, the adaptive frequency oscillator, which is able to adapt its intrinsic frequency to the resonant frequency of the body dynamics. After a small sidetrack to show that we can use adaptive frequency oscillators also for other applications than for adaptive controllers, namely for frequency analysis, we then come back to further investigation of the adaptive controller. We apply the same controller concept to a simple spring-mass hopper system. The spring-mass system consists of a body with two legs attached by rotational joints. The legs contain spring-damper elements. Finally, we present results of the implementation of the controller on a real robot, the experimental robot PUPPY II. This robot is a under-actuated robot with spring dynamics in the knee joints. It will be shown, that due to the appropriate simplification and concentration on relevant features in the toy-system the controller concepts works without a fundamental change on all systems from the toy system up to the real robot.