Biped Walking Robot Research Articles

A Comprehensive Survey of Push Recovery Techniques for Standing and Walking Bipedal Robots

من المتوقع للروبوتات ذات القدمين ان يكون لها دور فعال في حياتنا اليومية وايضا الدخول في المجلات الصناعية والطبية وان يكون لها دور في عملية البحث والانقاذ التي تتم في اماكن خطرة على البشر. من اهم العوائق التي تواجه هذا النوع من الروبوتات هو قدرتها على ان تبقى متوازنة ومستقرة في حالة وجود مؤثرات خارجية كالارض الغير مستوية او تعرضها الى اصطدام مع البيئة المحيطة وهذا مايعيق قدرتها على التفاعل مع المحيط. لذلك من الضروري جدا تصيميم انظمة سيطرة فعالة قادرة على مساعدة هذه الروبوتات بان تبقى متوازنة اثناء تفاعلها مع البيئة المحيطة. هذه الدراسة تقدم مراجعة ادبية للبحوث السابقة الي عملت على فكرة ابقاء الروبوتات ذات القدمين مستقرة عندما تكون واقفة او متحركة (المشي) واثناء وجود قوة خارجية تحاول ان تجعل الروبوتات غير متوازنة. بالاضافة الى ذلك، هذه الورقة تقدم تصنيف واضح للدراسات السابقة بالاعتماد على ثلاثة معايير(التمثيل الرياضي للروبوت والطريقة المستخدمة للسيطرة والمتحسسات المطلوبة). هذا التصنيف سوف يساعد الباحثين على فهم وتحليل الطرق المستخدمة بطريقة سريعة وفعالة.

When and where to step: Terrain-aware real-time footstep location and timing optimization for bipedal robots

Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has focused on optimizing the footstep placement while keeping the step timing constant. In this work, we introduce a footstep planner capable of optimizing footstep placement and step time online. The proposed planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer based on Augmented Lagrangian (AL) method with analytical gradient descent, solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real time to optimize for footstep location as well as step timing at the rate of 200 Hz. We show that such asynchronous real-time optimization with the AL method (ARTO-AL) provides the required robustness and speed for successful online footstep planning. Furthermore, ARTO-AL can be extended to plan footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains. Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL demonstrates increased stability in simulated walking experiments as it can resist pushes on flat ground and on a 10° ramp up to 120 N and 100 N respectively. Videos22https://youtu.be/2pwDTEJzmjI. and open-source code33https://github.com/WangKeAlchemist/ARTO-AL/tree/master. are released.

Bipedal robot center of pressure feedback simulation for center of mass learning

This research aims to create a walking bipedal robot with center of pressure feedback simulation for the center of mass learning, describe its feasibility for learning, describe students' motivation to learn, and describe students' science literacy after using it. The research method used ADDIE (analysis, design, development, implementation, and evaluation). The research data was obtained using a motivation scale questionnaire, science literacy scale, and feasibility scale. The research sample was 48 people; after the research obtained, the simulation of bipedal robot pressure center feedback for center of mass learning can be implemented with the principle of the robot's center of mass detected on the sole of the robot's foot equipped with a force sensitive resistor (FSR) sensor, the position of the center of mass is visible on the monitor screen as a center of mass learning, so that it can motivate students to learn and improve students' science literacy. This can be seen from the feasibility scale score, motivation scale, and science literacy scale of 4.133, 4.072, and 4.067 (scale 1 to 5), respectively, in the "good" category.

Biped Gait Stability Classification Based on the Predicted Step Viability.

In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost.

Precise Calculation of Inverse Kinematics of the Center of Gravity for Bipedal Walking Robots

The walking of humanoid robots is dependent on the precise tracking of their center of gravity and foot trajectories. Trajectory tracking is achieved by mobilizing their joints to achieve the correct trajectory. Errors occur because of assumptions on tracking the center of gravity and the foot trajectories. In this study, a numerical algorithm was developed that produces an exact and single kinematic solution in which the center of gravity and foot trajectories can be tracked with the desired precision. The effectiveness of this algorithm was examined with a dynamic simulation and compared with a method given in the literature. The main highlight of this study, using the presented algorithm, is that the robot could walk even if the position of its center of gravity was lower than its hips, resulting in a tracking error that was smaller than that reported in the literature.

Development of a Hand-Fan-Shaped Arm and a Model Predictive Controller for Leg Crossing, Walking, and One-Legged Balancing of a Wheeled-Bipedal Jumping Robot

Bipedal walking robots are advancing research by performing challenging human-like movements in complex environments. Particularly, wheeled-bipedal robots are used in many indoor environments by overcoming the speed and maneuverability limitations of bipedal walking robots without wheels. However, when both wheels lose contact with the ground, maintaining lateral balance becomes challenging, and there is an increased risk of toppling over. Furthermore, utilizing robotic arms similar to human arms, in addition to wheel-based balance, could enable more precise and stable control. In this paper, we develop a wheeled-bipedal robot that is able to jump and drive while also being able to cross its legs and balance on one leg (the OLEBOT). The OLEBOT is designed with a hand-fan-shaped end-effector capable of generating compensatory torque. By tilting the hand-fan-shaped end-effector in the opposite direction, OLEBOT achieves pitch control and single-leg balance. In jumping scenario, it imitates the arm movements of a person performing stationary high jumps, while utilizing a cam-based leg joint system to boost jump height. In addition, this paper develops a control architecture based on model predictive control (MPC) to ensure stable posture in driving, jumping, and one-legged balancing scenarios for OLEBOT. Finally, the experimental results demonstrate that OLEBOT is capable of maintaining a stable posture using a wheeled-bipedal system and achieving balance in a one-legged stance.

Controlling flat-foot limit cycle walkers with compliant joints based on local stability variation.

This study investigates local stability of a four-link limit cycle walking biped with flat feet and compliant ankle joints. Local stability represents the behavior along the solution trajectory between Poincare sections, which can provide detailed information about the evolution of disturbances. The effects of ankle stiffness and foot structure on local stability are studied. In addition, we apply a control strategy based on local stability analysis to the limit cycle walker. Control is applied only in the phases with poor local stability. Simulation results show that the energy consumption is reduced without sacrificing disturbance rejection ability. This study may be helpful in motion control of limit cycle bipedal walking robots with flat feet and ankle stiffness and understanding of human walking principles.

Compliant gait control method based on CVSLIP-FF model for biped robot walking over uneven terrain

Bipedal walking over uneven terrain remains a challenging task due to the environmental complexity and unavoidable landing impact. To realize the stable and robust walking of biped robots, this paper proposes a compliant gait control method, which focuses on walking compliance and conducts research on two levels. In the gait generation level, a Continuous-Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (CVSLIP-FF) model is provided with the consideration of the ankle joint and compliant spring-loaded leg. Then, a CVSLIP-FF based gait generation pattern with relevant walking strategies is provided to enhance the mobility of biped robots. In the joint control level, an ankle joint admittance control strategy is applied to achieve compliant robot-environment interaction. Experimental results indicate that compared with the traditional SLIP model, the proposed method performs better adaptability to uneven terrain with a 217.77% improvement, and enables biped robots to cope with slight unknown disturbance.

Small‐gain based stabilizing control for hybrid systems: Application to bipedal walking robot

AbstractThis study presents a systematic methodology for developing a stabilizing controller for a general hybrid systems model. The approach is based on utilizing the small‐gain theorem as a means of constructing the Lyapunov function and analyzing the input–output stability of the subsystems in the feedback loop. By considering the control system in a closed‐loop configuration with the hybrid system, the small‐gain theorem can be applied. In this scheme, a dynamic control system is proposed that satisfies the closed‐loop stability conditions. This method applies to various hybrid systems' applications due to its generality. To demonstrate the effectiveness and performance of the proposed control approach, two simulation examples, including a linear hybrid system and a bipedal walking robot, are examined.

Optimization of Biped Robot Walking Based on the Improved Particle Swarm Algorithm

The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm’s exploration and exploitation capabilities. The BPSO algorithm’s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO‐based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG‐controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.

Control of Dynamic Biped Walking Robot

The objective of this article is to present control algorithms of biped dynamic walking robot. A mathematical model of the walking robot is compiled. The construction of the robot is based on minimalist design principles - absolutely solid body with two legs. Analytical dependencies between six coordinates of the body, coordinates of the left and right legs and absolute coordinates of the feet are defined. The block diagram of control system is presented. Differential equations of walking are compiled. The dynamic stability of biped walking robot near the required trajectory is provided by discrete control of the parameters: «when and where to put foot». To define these parameters, it is proposed to use an inverted mathematical pendulum as an ideal mechanism. Its linearized differential equations allow present the feet coordinates according to the measured initial and final parameters (at the begging and at the end of the step). The article paid particular attention to results of such control method with various walking modes. The coordinates of the robot body, its velocities and accelerations, angles of rotation were investigated. Calculations confirmed the small deviations of the proposed model of walking from the ideal one. The developed methodology allows improve the construction and control system of biped dynamic walking robots at the design stage. Some of the tasks, it can be used: design machines simulating human walking, with high speed and maneuverability, with point feet and synchronized with natural uncontrolled movements.

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots.

Achieving omnidirectional walking for bipedal robots is considered one of the most challenging tasks in robotics technology. Reinforcement learning (RL) methods have proved effective in bipedal walking tasks. However, most existing methods use state machines to switch between multiple policies and achieve omnidirectional gait, which results in shaking during the policy switching process for bipedal robots. To achieve a seamless transition between omnidirectional gait and transient motion for full-size bipedal robots, we propose a novel multi-agent RL method. Firstly, a multi-agent RL algorithm based on the actor-critic framework is designed, and policy entropy is introduced to improve exploration efficiency. By learning agents with parallel initial state distributions, we minimize reliance on gait planner effectiveness in the Robot Operating System (ROS). Additionally, we design a novel heterogeneous policy experience replay mechanism based on Euclidean distance. Secondly, considering the periodicity of bipedal robot walking, we develop a new periodic gait function. Including periodic objectives in the policy can accelerate the convergence speed of training periodic gait functions. Finally, to enhance the robustness of the policy, we construct a novel curriculum learning method by discretizing Gaussian distribution and incorporate it into the robot's training task. Our method is validated in a simulation environment, and the results show that our method can achieve multiple gaits through a policy network and achieve smooth transitions between different gaits.

Complex dynamics of the passive biped robot with flat feet: Gait bifurcation, intermittency and crisis

During the past decades, the walking dynamics of different types of passive biped robots have garnered significant attention. This paper investigates the complex dynamics of a passive flat-footed robot, encompassing both the local bifurcation of limit cycles and global bifurcation, resulting in significant changes in the system’s walking pattern. To identify the robot’s dynamic behavior, the Lyapunov Exponents (LEs) are calculated using the small orthogonal perturbation vectors method. The obtained LEs diagram also unveils several subtle periodic windows scattered within the chaotic region, where interior crisis and intermittency occur. Moreover, two new kinds of gaits that coexist with the traditional period-1 ones are found. Of particular significance is the discovery of the double boundary crisis that caused by the critical period-3 unstable periodic orbit, and this crisis serves as the fundamental reason to the falling down on slopes of the flat-footed robots. By gaining a better understanding of the nature of passive dynamic walking, it becomes possible to design more efficient controllers that impose less stringent torque requirements on biped walking robots.

Adaptive robust control for biped walking under uncertain external forces

Adaptability and robustness are important expressions of the intelligent walking ability of biped robots. This paper is concerned with the problem of dynamical biped walking and robust control of biped robots under external forces. Due to the characteristics of strong coupling and hybrid, the robotic system is modeled as a rigid kinematic chain with Lagrange equations. A novel adaptive feedback controller is proposed based on sliding mode control (SMC) and hybrid zero dynamics. The novelty of the proposed work lies in taking the uncertainty of upper-bound error into consideration. The hybrid robust control is mentioned to approximate unknown dynamic functions with the adaptive weight. The restricted Poincare return map is utilized to analyze the stability of a nonlinear impulsive system. It ensures that the flow of the continuous subsystem can pass through the impact cross section. Finally, the simulation results illustrate that the proposed adaptive SMC control system can favorably track the reference trajectories, even when a fault occurs, which verifies the effectiveness of the proposed method.

Robust Walking and Running Gaits for Biped Robots with a QP-Based Whole-Body Controller

This paper presents a new control approach for a humanoid biped robot to perform highly dynamic locomotion and keep balance under large external disturbances. The proposed control approach mainly contains two parts: a simplified model-based task planner and an online whole-body controller. The periodic stability is achieved with a foot placement policy presented in this paper. The landing position is predicted and adjusted at every time step to adapt to uncertain disturbances. Then the desired task trajectories are tracked with a quadratic programming (QP) based task space whole-body controller, while satisfying some specific physical constraints. The whole-body controller outputs the optimized joint accelerations, which are then used to produce desired joint torques for the robot’s actuators. The positions of the center of mass (CoM) along sagittal and lateral axes are not controlled in the whole-body controller. Meanwhile, the torques at the ankles are constrained to be small and the robot follows a passive-like behavior, which is energetically efficient and helps the feet of the robot to fit better in the environment. Simulations on several highly dynamic gaits have been conducted, two examples among which are presented in this paper. The first demonstrates a walking gait with an increasing sagittal speed and its capability to resist large external disturbances. In the second example, the robot is able to run stably on uneven terrain without knowing any information about it.

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

Bipedal walking robot platform with a vertically stabilized base usable as a bipedchair for disabled people

Bipedal walking robot platform with a vertically stabilized base usable as a bipedchair for disabled people

Optimization of the Parameters of Feet and the Laws of Motion of Bipedal Walking Robots

Optimization of the Parameters of Feet and the Laws of Motion of Bipedal Walking Robots

An Input-to-State Stability Perspective on Robust Locomotion

Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a bipedal robot to locomote in the presence of such disturbances. This motivates the need for analytical tools aimed at generalized notions of stability – robustness. Towards this, we propose a novel definition of robustness, termed δ-robustness, to characterize the domain on which a nominal periodic orbit remains stable despite uncertain terrain. This definition is derived by treating perturbations in ground height as disturbances in the context of the input-to-state-stability (ISS) of the extended Poincaré map associated with an orbit. The main theoretic result is the formulation of robust Lyapunov functions that certify δ-robustness of periodic orbits. This yields an optimization framework for verifying δ-robustness, which is demonstrated in simulation with a bipedal robot walking on uneven terrain.

Occurrence of Complex Behaviors in the Uncontrolled Passive Compass Biped Model

It is widely known that an appropriately built unpowered bipedal robot can walk down an inclined surface with a passive steady gait. The features of such gait are determined by the robot's geometry and inertial properties, as well as the slope angle. The energy needed to keep the biped moving steadily comes from the gravitational potential energy as it descends the inclined surface. The study of such passive natural motions could lead to ideas for managing active walking devices and a better understanding of the human locomotion. The major goal of this study is to further investigate order, chaos and bifurcations and then to demonstrate the complexity of the passive bipedal walk of the compass-gait biped robot by examining different bifurcation diagrams and also by studying the variation of the eigenvalues of the Poincaré map's Jacobian matrix and the variation of the Lyapunov exponents. We reveal also the exhibition of some additional results by changing the inertial and geometrical parameters of the bipedal robot model.