Footstep adaptation strategy for reactive omnidirectional walking in humanoid robots

Robotica ◽  
2017 ◽  
Vol 36 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Jiwen Zhang ◽  
Zeyang Xia ◽  
Li Liu ◽  
Ken Chen
Keyword(s):  
Optimization Problem ◽  
Center Of Mass ◽  
Humanoid Robots ◽  
Adaptation Strategy ◽  
Vehicle Model ◽  
Risk Of Falling ◽  
Response Quality ◽  
Foot Placement ◽  
Planning Framework ◽  
Simulation Based

SUMMARYStability, high response quality and rapidity are required for reactive omnidirectional walking in humanoids. Early schemes focused on generating gaits for predefined footstep locations and suffered from the risk of falling over because they lacked the ability to suitably adapt foot placement. Later methods combining stride adaptation and center of mass (COM) trajectory modification experienced difficulties related to increasing computing loads and an unwanted bias from the desired commands. In this paper, a hierarchical planning framework is proposed in which the footstep adaption task is separated from that of COM trajectory generation. A novel omnidirectional vehicle model and the inequalities deduced therefrom are adopted to describe the inter-pace connection relationship. A constrained nonlinear optimization problem is formulated and solved based on these inequalities to generate the optimal strides. A black-box optimization problem is then constructed and solved to determine the model constants using a surrogate-model-based approach. A simulation-based verification of the method and its implementation on a physical robot with a strictly limited computing capacity are reported. The proposed method is found to offer improved response quality while maintaining rapidity and stability, to reduce the online computing load required for reactive walking and to eliminate unnecessary bias from walking intentions.

Dynamic and Reactive Walking for Humanoid Robots Based on Foot Placement Control

2016 ◽  
Vol 13 (02) ◽  
pp. 1550041 ◽  
Author(s):  
Juan Alejandro Castano ◽  
Zhibin Li ◽  
Chengxu Zhou ◽  
Nikos Tsagarakis ◽  
Darwin Caldwell
Keyword(s):  
Center Of Mass ◽  
Humanoid Robots ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Foot Placement ◽  
Inverted Pendulum Model ◽  
Gait Parameters ◽  
Pendulum Model ◽  
Single Mass ◽  
Walking Velocity

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

scholarly journals Capture Steps: Robust Walking for Humanoid Robots

2019 ◽  
Vol 16 (06) ◽  
pp. 1950032 ◽  
Author(s):  
Marcell Missura ◽  
Maren Bennewitz ◽  
Sven Behnke
Keyword(s):  
Degrees Of Freedom ◽  
Center Of Mass ◽  
Humanoid Robots ◽  
Open Loop ◽  
Bipedal Walking ◽  
Foot Placement ◽  
Inverted Pendulum Model ◽  
Push Recovery ◽  
Pendulum Model ◽  
Walking Velocity

Stable bipedal walking is a key prerequisite for humanoid robots to reach their potential of being versatile helpers in our everyday environments. Bipedal walking is, however, a complex motion that requires the coordination of many degrees of freedom while it is also inherently unstable and sensitive to disturbances. The balance of a walking biped has to be constantly maintained. The most effective ways of controlling balance are well timed and placed recovery steps — capture steps — that absorb the expense momentum gained from a push or a stumble. We present a bipedal gait generation framework that utilizes step timing and foot placement techniques in order to recover the balance of a biped even after strong disturbances. Our framework modifies the next footstep location instantly when responding to a disturbance and generates controllable omnidirectional walking using only very little sensing and computational power. We exploit the open-loop stability of a central pattern generated gait to fit a linear inverted pendulum model (LIPM) to the observed center of mass (CoM) trajectory. Then, we use the fitted model to predict suitable footstep locations and timings in order to maintain balance while following a target walking velocity. Our experiments show qualitative and statistical evidence of one of the strongest push-recovery capabilities among humanoid robots to date.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals Turning Gait Planning Method for Humanoid Robots

2018 ◽  
Vol 8 (8) ◽  
pp. 1257 ◽  
Author(s):  
Tianqi Yang ◽  
Weimin Zhang ◽  
Xuechao Chen ◽  
Zhangguo Yu ◽  
Libo Meng ◽  
...  
Keyword(s):  
Inverted Pendulum ◽  
Center Of Mass ◽  
Translational Motion ◽  
Humanoid Robots ◽  
Inverted Pendulum Model ◽  
Straight Line ◽  
Pendulum Model ◽  
The Stability ◽  
And Control ◽  
Present Simulation

The most important feature of this paper is to transform the complex motion of robot turning into a simple translational motion, thus simplifying the dynamic model. Compared with the method that generates a center of mass (COM) trajectory directly by the inverted pendulum model, this method is more precise. The non-inertial reference is introduced in the turning walk. This method can translate the turning walk into a straight-line walk when the inertial forces act on the robot. The dynamics of the robot model, called linear inverted pendulum (LIP), are changed and improved dynamics are derived to make them apply to the turning walk model. Then, we expend the new LIP model and control the zero moment point (ZMP) to guarantee the stability of the unstable parts of this model in order to generate a stable COM trajectory. We present simulation results for the improved LIP dynamics and verify the stability of the robot turning.

Capturability of Inverted Pendulum Gait Model Under Slip Conditions

2018 ◽  
Author(s):  
Marko Mihalec ◽  
Jingang Yi
Keyword(s):  
Reaction Time ◽  
Coefficient Of Friction ◽  
Inverted Pendulum ◽  
Center Of Mass ◽  
Control Input ◽  
Vertical Movements ◽  
Foot Placement ◽  
Slip Conditions ◽  
The Coefficient Of Friction ◽  
Stable Gait

This paper presents a simple inverted pendulum gait model to study walking under slip conditions. The model allows for both the horizontal and vertical movements of the center of mass during normal walking and walking gaits with foot slip. Stability of the system is analyzed using the concept of capturability. Considering foot placement as a control input, we obtain the stable regions which lead to stable gait. The size of those stable regions is used to evaluate the effect of the coefficient of friction and the slip reaction time on capturability. We also analyze the feasibility of recovery from slip gait in relation to the coefficient of friction and the reaction time. The results confirm the effectiveness of the model and the capturability developement.

scholarly journals Active foot placement control ensures stable gait: Effect of constraints on foot placement and ankle moments

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242215
Author(s):  
A. M. van Leeuwen ◽  
J. H. van Dieën ◽  
A. Daffertshofer ◽  
S. M. Bruijn
Keyword(s):  
Steady State ◽  
Center Of Pressure ◽  
Center Of Mass ◽  
Reaction Force ◽  
Stride Frequency ◽  
Tight Control ◽  
Foot Placement ◽  
Ankle Moment ◽  
Reaction Forces ◽  
Moment Control

Step-by-step foot placement control, relative to the center of mass (CoM) kinematic state, is generally considered a dominant mechanism for maintenance of gait stability. By adequate (mediolateral) positioning of the center of pressure with respect to the CoM, the ground reaction force generates a moment that prevents falling. In healthy individuals, foot placement is complemented mainly by ankle moment control ensuring stability. To evaluate possible compensatory relationships between step-by-step foot placement and complementary ankle moments, we investigated the degree of (active) foot placement control during steady-state walking, and under either foot placement-, or ankle moment constraints. Thirty healthy participants walked on a treadmill, while full-body kinematics, ground reaction forces and EMG activities were recorded. As a replication of earlier findings, we first showed step-by-step foot placement is associated with preceding CoM state and hip ab-/adductor activity during steady-state walking. Tight control of foot placement appears to be important at normal walking speed because there was a limited change in the degree of foot placement control despite the presence of a foot placement constraint. At slow speed, the degree of foot placement control decreased substantially, suggesting that tight control of foot placement is less essential when walking slowly. Step-by-step foot placement control was not tightened to compensate for constrained ankle moments. Instead compensation was achieved through increases in step width and stride frequency.

scholarly journals Optimum range of angle tracking radars: a theoretical computing

2019 ◽  
Vol 9 (3) ◽  
pp. 1765 ◽  
Author(s):  
Sadegh Samadi ◽  
Mohammad Reza Khosravi ◽  
Jafar A. Alzubi ◽  
Omar A. Alzubi ◽  
Varun G. Menon
Keyword(s):  
Closed Form ◽  
Optimization Problem ◽  
Tracking System ◽  
Total Error ◽  
Optimal Range ◽  
The Past ◽  
Simple Simulation ◽  
Simulation Based ◽  
Angle Tracking ◽  
Theoretical Computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other.

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