Walking Pattern Generation Research Articles

3P1-G06 ヒューマノイドにおける二足歩行行動から腕接地歩行への拡張に基づく多種歩容行動の実現(ヒューマノイド(2))

In order to enable humanoids to walk steadily, it is important to use various walking forms. For examples, humanoids should walk not in biped but in quadruped when the ground is rough or they cannot believe that the space before them is and will be clear. With having choices of walking forms, they can access wider area by therselves. In this paper, we propose a way to extend a conventional biped walking pattern generator to a generalized walking pattern generator under arm/leg coordination by forcusing on gait pattern and the way to calculate target ZMP trajectory, and conduct a walking experiment over uneven ground by switching a walking form from biped to quadruped. Also we propose an attitude stabilization control system for walking over rougher ground and conduct a simple test for its validation.

Modifiable Walking Pattern Generation Handling Infeasible Navigational Commands for Humanoid Robots

In order to accomplish complex navigational commands, humanoid robot should be able to modify its walking period, step length and direction independently. In this paper, a novel walking pattern generation algorithm is proposed to satisfy these requirements. Modification of the walking pattern can be considered as a transition between two periodic walking patterns, which follows each navigational command. By assuming the robot as a linear inverted pendulum, the equations of motion between ZMP(Zero Moment Point) and CM(Center of Mass) state is easily derived and analyzed. After navigational command is translated into the desired CM state, corresponding CM motion is generated to achieve the desired state by using simple ZMP functions. Moreover, when the command is not feasible, feasible command is alternated by using binary search algorithm. Subsequently, corresponding CM motion is generated. The effectiveness of the proposed algorithm is verified by computer simulation.

1A1-S02 最適化手法に基づく障害物環境に対応した2足ロボット歩行動作生成(脚移動ロボット)

1A1-S02 最適化手法に基づく障害物環境に対応した2足ロボット歩行動作生成(脚移動ロボット)

Modeling, stability and walking pattern generators of biped robots: a review

SUMMARYBiped robots have gained much attention for decades. A variety of researches have been conducted to make them able to assist or even substitute for humans in performing special tasks. In addition, studying biped robots is important in order to understand human locomotion and to develop and improve control strategies for prosthetic and orthotic limbs. This paper discusses the main challenges encountered in the design of biped robots, such as modeling, stability and their walking patterns. The subject is difficult to deal with because the biped mechanism intervenes with mechanics, control, electronics and artificial intelligence. In this paper, we collect and introduce a systematic discussion of modeling, walking pattern generators and stability for a biped robot.

DEVELOPMENT AND WALKING CONTROL OF EMOTIONAL HUMANOID ROBOT, KIBO

This paper deals with the mechanical design, system integration, and dynamic walking algorithm of KIBO, an emotional biped humanoid robot that has a facial expression mechanism and various human-interactive devices. To emphasize the aesthetic features and marketability of KIBO, the mechanical design was performed after the exterior design stage to conform to all requirements, particularly constraints imposed by the external appearance and human-like link dimensions. For flexible biped walking, a walking pattern generator with variable walking parameters was developed. The walking pattern generator generates both a walking pattern and a corresponding reference zero-moment point (ZMP) pattern simultaneously. For stable biped walking, a walking control strategy using the ZMP and inertial sensor data was developed. In the strategy, we newly proposed a dual ZMP control approach and a posture control approach using an equivalent body inclination, which is calculated from the ZMP and inertial sensor data for robust walking on non-level ground. Finally, the hardware, software architecture, and dynamic walking performance of KIBO were verified through several walking experiments.

Biped Walking Pattern Generation by Using Preview Control Based on Three-Mass Model

Biped walking robots have been widely researched because biped walking is considered as an effective way to locomote in human environments. There are a lot of ways to generate biped walking patterns, and it is widely known that the model certainty of the robot and the immediate generation of the trajectory are very important. Using preview control is a useful way to generate the walking patterns. By using the preview control method, modeling errors, which are generated by using a simple model, can be compensated by using the preview controller again in the conventional approaches. As a matter of fact, it is known that it takes much time to generate the trajectory by using the preview controller again. In this paper, biped walking pattern generation by using preview control based on a three-mass model is proposed. By using the proposed method, the modeling errors can be decreased without using the preview controller again, which means that the immediate generation of the trajectory can be achieved. The validity of the proposed method is confirmed by numerical examples and experimental results.

Humanoid motion generation and swept volumes: theoretical bounds for safe steps

In the context of humanoid robot footstep planning based on a continuous action set, we conduct an analysis of the sensitivity of a walking pattern generator. Given a variation of an input vector, we calculate a bound on the variation of the volume swept by the robot lower body during the corresponding actions (steps). Since the input vector depends on real parameters, there is an infinite number of possible steps, but the calculated bound permits a sound and safe use of only a finite number of swept volumes to account for all the possible motions. After numerical evaluations, we discuss potential applications.

Walking pattern generation for a humanoid robot with compliant joints

This work presents a walking pattern generator based on the control of the center of mass (COM) states and its experimental validations on the compliant humanoid robot COMAN powered by intrinsically compliant joints. To cope with the inaccuracies of the joint position tracking resulted by the physical compliance, the proposed pattern generator uses the feedback states of the COM and on-line computes the updated COM references. The position and velocity of the COM are the state variables, and the constrained ground reaction force (GRF) limited by the support polygon is the control effort to drive the real COM states to track the desired references. The frequency analysis of the COM demonstrates its low frequency spectrum that indicates the demand of a low control bandwidth which is suitable for a robot system with compliant joints. The effectiveness of the proposed gait generation method was demonstrated by the experiments performed on the COMAN robot. The experimental data such as the COM position and velocity tracking, the GRF applied on feet, the measured step length and the walking velocity are analyzed. The effect of the passive compliance is also discussed.

3-D Command State-Based Modifiable Bipedal Walking on Uneven Terrain

Bipedal robots in previous research work were unable to independently modify the elements of a walking pattern on uneven terrain without any extra footstep for adjusting the center of mass (COM) motion. To solve this problem, this paper extends the modifiable walking pattern generator (MWPG). The MWPG can independently modify the elements of the walking pattern without any extra footstep on flat terrain only. The extended MWPG can be applied on uneven terrain. In the extended MWPG, a 3-D command state is defined to generate the walking pattern on uneven terrain. Instead of using the constant COM height, the vertical COM trajectory is generated to satisfy the foot height of the swing leg. Also, an additional trajectory, generated by the cubic spline interpolation, is supplied to the vertical foot trajectory of the MWPG. The proposed MWPG is implemented on the small-sized bipedal robot, HanSaRam-IX (HSR-IX) and the effectiveness of the proposed MWPG is demonstrated through the experiment.

State-incremental optimal control of 3D COG pattern generation for humanoid robots

Many researchers have proposed walking pattern generation methods with zero moment point – center of gravity (ZMP–COG) constraints. Some of the researchers used a neural-networks (NN), a central pattern generator (CPG), or a genetic algorithm (GA) for ZMP–COG pattern generation. However, the parameters used in those methods are too many, and the procedure to learn or to search them costs too much computation time. Other researchers designed controllers or used analytical solution method to generate COG trajectories. These methods generate the ZMP-COG pattern very quickly, but the COG height is limited to a constant to linearize the inverted pendulum model of the robot. Due to this limitation, the robots cannot walk freely on surfaces that change in height. To solve this problem, researchers start to use the original nonlinear inverted pendulum model to make the COG height changeable such as using a numerical method or a feedback controller. In this paper, an optimal control-based pattern generator that can allow COG height change is proposed. It can solve sagittal and lateral COG patterns with arbitrarily assigned COG height and ZMP trajectories in real time. Thus, dynamic walking on height-changing surfaces can be achieved.

A Simple Feedback Controller to Reduce Angular Momentum in ZMP-Based Gaits

The Zero Moment Point (ZMP) stability criterion has been broadly employed for walking pattern generation in legged robots. However, ZMP-based approaches usually ignore the presence of angular momentum in the system. This hinders the performance of the gait, especially against disturbances. In this work we propose an angular momentum controller that can be integrated into standard ZMP-based gaits. The experiments on a real Nao robot demonstrate that the use of the proposed angular momentum controller improves the stability of a walking pattern generator based on the preview control of the ZMP.

On the Walking Pattern Generators of Biped Robot

On the Walking Pattern Generators of Biped Robot

Realization of a Biped Robot Lower Limb Walking without Double Support Phase on Uneven Terrain

Zero moment point (ZMP) is widely used in dynamical walking control of the biped robot, but it is hard to obtain the ZMP exactly. The paper describes a simple walking control method without using ZMP information directly. Firstly, the paper introduced a biped robot lower-limb prototype which is driven by linear hydraulic servocylinder. Then the paper simplifies the walking control in the lateral plane with a simple walking pattern generation method named “dynamic equilibrium method,” which is fit for active and underactuated biped robots. In the following section the paper provides the balance control methods without using ZMP information directly. Finally, simulation experiments with MD.DAMS and experiments in physical prototype are given. The experimental results confirm the effectiveness of the proposed control methods.

Motion Planning for Humanoid Robots in Complex Environments Based on Stance Sequence and ZMP Reference

It is a great challenge to plan motion for humanoid robots in complex environments especially when the terrain is cluttered and discrete. To address this problem, a novel method is proposed in this paper by planning the gait according to the stance sequence and ZMP (Zero Moment Point) reference. It consists of two components: an adaptive footstep planner and a walking pattern generator. The adaptive footstep planner can generate the stance path according to the walking rules and adjust the orientation of body relevantly. As the footstep locations are determined, Linear Inverted Pendulum Model (LIPM) is used to generate the walking pattern with a moving ZMP reference. As demonstrated in experiments on the humanoid robot HOAP-2, our method can successfully plan footstep trajectories as well as generate the stable and natural-looking gait in typical cluttered and discrete environments.

Voluntary and Reactive Recruitment of Locomotor Muscle Synergies during Perturbed Walking

The modular control of muscles in groups, often referred to as muscle synergies, has been proposed to provide a motor repertoire of actions for the robust control of movement. However, it is not clear whether muscle synergies identified in one task are also recruited by different neural pathways subserving other motor behaviors. We tested the hypothesis that voluntary and reactive modifications to walking in humans result from the recruitment of locomotor muscle synergies. We recorded the activity of 16 muscles in the right leg as subjects walked a 7.5 m path at two different speeds. To elicit a second motor behavior, midway through the path we imposed ramp and hold translation perturbations of the support surface in each of four cardinal directions. Variations in the temporal recruitment of locomotor muscle synergies could account for cycle-by-cycle variations in muscle activity across strides. Locomotor muscle synergies were also recruited in atypical phases of gait, accounting for both anticipatory gait modifications before perturbations and reactive feedback responses to perturbations. Our findings are consistent with the idea that a common pool of spatially fixed locomotor muscle synergies can be recruited by different neural pathways, including the central pattern generator for walking, brainstem pathways for balance control, and cortical pathways mediating voluntary gait modifications. Together with electrophysiological studies, our work suggests that muscle synergies may provide a library of motor subtasks that can be flexibly recruited by parallel descending pathways to generate a variety of complex natural movements in the upper and lower limbs.

Feasibility Check of an Assist System Through the Simulation of Bipedal Walking Using a CPG

A wearable mobile-base Walking Assist System (WAS) is simulated in this paper with the bipedal simulator we developed. The simulator employs a Central Pattern Generator (CPG) for bipedal walking pattern generation. The CPG-based walking pattern is one of the candidates for simulating human walking. Average Japanese body dimension data is applied to the bipedal model so that walking efficiency can be evaluated using the simulator. The effectiveness of the proposed simulator is confirmed by comparing real human walking and simulated walking in terms of the shape of the swing leg trajectory, data from the pressure sensor, and the feasibility of the prototype WAS. A prototype is developed and experimental results show the effectiveness of the bipedal simulator.

Autonomous navigation of a humanoid robot over unknown rough terrain using a laser range sensor

The present paper describes the integration of laser-based perception, footstep planning, and walking control of a humanoid robot for navigation over previously unknown rough terrain. A perception system that obtains the shape of the surrounding environment to an accuracy of a few centimeters is realized based on input obtained using a scanning laser range sensor. A footstep planner decides the sequence of stepping positions using the obtained terrain shape. A walking controller that can cope with a few centimeters of error in terrain shape measurement is achieved by combining the generation of a 40-ms cycle online walking pattern and a ground reaction force controller with sensor feedback. An operational interface was developed to send commands to the robot. A mixed-reality display was adopted to realize an intuitive interface. The navigation system was implemented on the HRP-2, a full-size humanoid robot. The performance of the proposed system for navigation over unknown rough terrain and the accuracy of the terrain shape measurement were investigated through several experiments.

AN EVOLUTIONARY OPTIMIZED FOOTSTEP PLANNER FOR THE NAVIGATION OF HUMANOID ROBOTS

In this paper, an evolutionary optimized footstep planner for the navigation of humanoid robots is proposed. A footstep planner based on a univector field navigation method is proposed to generate a command state (CS) as an input to a modifiable walking pattern generator (MWPG) at each footstep. The MWPG generates associated trajectories of every leg joint to follow the given CS. In order to satisfy various objectives in the navigation, the univector fields are optimized by evolutionary programming. The three objectives, shortest elapsed time to get to a destination, safety without obstacle collision, and less energy consumption, are considered with mechanical constraints of a real humanoid robot, that is, the maximum step length and allowable yawing range of the feet. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment for a small-sized humanoid robot, HanSaRam-IX.

Dependable Humanoid Navigation System Based on Bipedal Locomotion

In this paper, a dependable humanoid navigation system is proposed by considering many difficulties in humanoid navigation based on bipedal locomotion in an uncertain environment. In particular, we propose a layered architecture to resolve complicated problems through a hierarchical manner. Within the proposed software architecture, a walking path planner, a walking footstep planner, and a walking pattern generator are integrated in a hierarchy to create a reliable motion that overcomes foot slippage and localization sensor noise. Each layer is designed to overcome difficulties originating from bipedal locomotion such as unstable dynamics, inclusion of a sinusoidal noise component in the localization sensor measurement, and disturbance regarding discrete footstepping. The designed navigation system is implemented on a human-sized experimental humanoid platform and is tested for the evaluation of its reliability and robustness in various tasks.

Fast Humanoid Robot Collision-Free Footstep Planning Using Swept Volume Approximations

In this paper, we propose a novel and coherent framework for fast footstep planning for legged robots on a flat ground with 3-D obstacle avoidance. We use swept volume approximations that are computed offline in order to considerably reduce the time spent in collision checking during the online planning phase, in which a rapidly exploring random tree variant is used to find collision-free sequences of half-steps (which are produced by a specific walking pattern generator). Then, an original homotopy is used to smooth the sequences into natural motions, gently avoiding the obstacles. The results are experimentally validated on the robot HRP-2.