Biped Walking Robot Research Articles

Fast and Safe Contact Establishment Strategy for Biped Walking Robot

Fast and Safe Contact Establishment Strategy for Biped Walking Robot

Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm.

Ankle push-off occurs when muscle–tendon units about the ankle joint generate a burst of positive power at the end of stance phase in human walking. Ankle push-off mainly contributes to both leg swing and center of mass (CoM) acceleration. Humans use the amount of ankle push-off to induce speed changes. Thus, this study focuses on determining the faster walking speed and the lowest energy efficiency of biped robots by using ankle push-off. The real-time-space trajectory method is used to provide reference positions for the hip and knee joints. The torque curve during ankle push-off, composed of three quintic polynomial curves, is applied to the ankle joint. With the walking distance and the mechanical cost of transport (MCOT) as the optimization goals, the genetic algorithm (GA) is used to obtain the optimal torque curve during ankle push-off. The results show that the biped robot achieved a maximum speed of 1.3 m/s, and the ankle push-off occurs at 41.27−48.34% of the gait cycle. The MCOT of the bipedal robot corresponding to the high economy gait is 0.70, and the walking speed is 0.54 m/s. This study may further prompt the design of the ankle joint and identify the important implications of ankle push-off for biped robots.

Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking.

Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for designing a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended motor command, in designing localised controllers in the CPG-based bipedal walking model. To this end, we developed a Tegotae function that quantifies the Tegotae concept. This function allowed incorporating decentralised controllers into the proposed bipedal walking model systematically. We designed a two-dimensional bipedal walking model using Tegotae functions and subsequently implemented it in simulations to validate the proposed design scheme. We found that our model can walk on both flat and uneven terrains and confirmed that the application of the Tegotae functions in all joint controllers results in excellent adaptability to environmental changes.

Adaptive Robust Tracking Control for Hybrid Models of Three-Dimensional Bipedal Robotic Walking Under Uncertainties

Abstract This paper introduces an adaptive robust trajectory tracking controller design to provably realize stable bipedal robotic walking under parametric and unmodeled uncertainties. Deriving such a controller is challenging mainly because of the highly complex bipedal walking dynamics that are hybrid and involve nonlinear, uncontrolled state-triggered jumps. The main contribution of the study is the synthesis of a continuous-phase adaptive robust tracking control law for hybrid models of bipedal robotic walking by incorporating the construction of multiple Lyapunov functions into the control Lyapunov function. The evolution of the Lyapunov function across the state-triggered jumps is explicitly analyzed to construct sufficient conditions that guide the proposed control design for provably guaranteeing the stability and tracking the performance of the hybrid system in the presence of uncertainties. Simulation results on fully actuated bipedal robotic walking validate the effectiveness of the proposed approach in walking stabilization under uncertainties.

Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor

A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior and designed to perform human specific tasks. Currently, humanoid robots are not capable to walk like human being. To perform the walking task, in the current work, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging and fast walk is collected through our configured IMU sensor and mobile-based accelerometers device. To capture the pattern for six different walking styles, data is extracted for hip, knee, ankle, shank, thigh and foot. A total six classes of walking activities are explored for clinical examination. The accelerometer is placed at center of the human body of 15 male and 10 female subjects. In the experimental setup, we have done exploratory analysis over the different gait capturing techniques, different gait features and different gait classification techniques. For the classification purpose, three state of art techniques are used as artificial neural network, extreme learning machine and deep neural network learning based CNN mode. The model classification accuracy is obtained as 87.4%, 88% and 92%, respectively. Here, WISDM activity data set is also used for verification purpose.

Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot

Real-time surface recognition has become a critical factor for ensuring safe walking of intelligent biped robots in a complex human living environment. This work aims at enabling wide cost-efficient implementation of sensing solutions for surface recognition via walking-pattern classification by restricting the necessary hardware to a cost-economic microprocessor and a single type of force sensors. For experimental analysis, we explored the walking-pattern classification performance using a framework which combines a support vector machine (SVM) and four time-domain feature descriptors, i.e., mean of amplitude (MA), integral of absolute value (IAV), variance (VAR), and root mean square (RMS). During the online pattern classification, the dynamical force-sensory-data stream was extracted using a real-time overlapped-window-based method. Multiple binary SVM classifiers were applied for solving the multi-class classification problem, due to the reasonably high accuracy and the relatively small complexity for hardware implementation, allowing simultaneous strength exploitation of above four individual feature descriptors with a one-versus-one (OVO) strategy. The experimental results, obtained with 250 samples/surface, verified 93.8% mean average precision, 93.7% average accuracy and recall rates of 98.8%, 91.6%, 82.0%, 98.0%, 98.0% for smooth wood, rough foam, smooth foam, thick carpet, and thin carpet, respectively. Only the dynamical force-sensing data were employed for a 10-fold cross validation, which enabled the high processing speed of 0.73 ms/stride. The developed cost-efficient and accurate surface-recognition system can be useful for ensuring safe in-door locomotion for the biped robot and can help the robot to better understand the human living environment by increasing its sensing diversity.

Trajectory Planning of Flexible Walking for Biped Robots Using Linear Inverted Pendulum Model and Linear Pendulum Model

Linear inverted pendulum model (LIPM) is an effective and widely used simplified model for biped robots. However, LIPM includes only the single support phase (SSP) and ignores the double support phase (DSP). In this situation, the acceleration of the center of mass (CoM) is discontinuous at the moment of leg exchange, leading to a negative impact on walking stability. If the DSP is added to the walking cycle, the acceleration of the CoM will be smoother and the walking stability of the biped will be improved. In this paper, a linear pendulum model (LPM) for the DSP is proposed, which is similar to LIPM for the SSP. LPM has similar characteristics to LIPM. The dynamic equation of LPM is also linear, and its analytical solution can be obtained. This study also proposes different trajectory-planning methods for different situations, such as periodic walking, adjusting walking speed, disturbed state recovery, and walking terrain-blind. These methods have less computation and can plan trajectory in real time. Simulation results verify the effectiveness of proposed methods and that the biped robot can walk stably and flexibly when combining LIPM and LPM.

Model-Based Adaptive Control of Transfemoral Prostheses: Theory, Simulation, and Experiments

This paper presents and experimentally implements three different adaptive and robust adaptive controllers as the first steps toward using model-based controllers for transfemoral prostheses. The goal of this paper is to translate these control methods to the robotic domain, from bipedal robotic walking to prosthesis walking, including a rigorous stability analysis. The human/prosthesis system is first modeled as a two-domain hybrid asymmetric system. An optimization problem is formulated to obtain a stable human-like gait. The proposed controllers are then developed for the combined human/prosthesis model and the optimized reference gait. The stability of all three controllers is proven using the Lyapunov stability theorem, ensuring convergence to the desired gait. The proposed controllers are first verified on a bipedal walking robot as a hybrid human/prosthesis model in simulation. They are then experimentally tested on a treadmill with an able-bodied subject using third iteration of AMBER Prosthetic (AMPRO3), a custom self-contained powered transfemoral prosthesis. Finally, outdoor tests are carried out using AMPRO3 with three test subjects walking on level ground, uphill slopes, and downhill slopes at slope angles of 3° and 8°, to demonstrate walking in different real-world environments.

Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization

Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great potential in solving robot skills learning problems. However, policy gradient methods require too many instances of robot online interaction with the environment in order to learn a good policy, which means lower efficiency of the learning process and a higher likelihood of damage to both the robot and the environment. In this paper, we propose a two-phase (imitation phase and practice phase) framework for efficient learning of robot walking skills, in which we pay more attention to the quality of skill learning and sample efficiency at the same time. The training starts with what we call the first stage or the imitation phase of learning, updating the parameters of the policy network in a supervised learning manner. The training set used in the policy network learning is composed of the experienced trajectories output by the iterative linear Gaussian controller. This paper also refers to these trajectories as near-optimal experiences. In the second stage, or the practice phase, the experiences for policy network learning are collected directly from online interactions, and the policy network parameters are updated with model-free reinforcement learning. The experiences from both stages are stored in the weighted replay buffer, and they are arranged in order according to the experience scoring algorithm proposed in this paper. The proposed framework is tested on a biped robot walking task in a MATLAB simulation environment. The results show that the sample efficiency of the proposed framework is much higher than ordinary policy gradient algorithms. The algorithm proposed in this paper achieved the highest cumulative reward, and the robot learned better walking skills autonomously. In addition, the weighted replay buffer method can be made as a general module for other model-free reinforcement learning algorithms. Our framework provides a new way to combine model-based reinforcement learning with model-free reinforcement learning to efficiently update the policy network parameters in the process of robot skills learning.

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm.

To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.

Developing the Mathematical Model of the Bipedal Walking Robot Executive Mechanism

The paper considers the accuracy of footstep control in the vicinity of the application object. The methodology of forming a simulation of the executive electro-hydraulic servomechanism is developed. The paper presents control algorithms in the dynamic walking mode. The issues of stabilization of the sensors installed in the soles are investigated. The description of the laboratory model and simulation of the main links of the exoskeleton, approximated to human parameters, allowing to insert the studied algorithms of motion of the executive mechanism into the program of automation of calculations of the links of motion are given. The authors for the first time simulated the bipedal walking robot using modern digital technologies, including the joint use of pneumatic electric drive. This paper proposes an automated control scheme for manipulators controlling immobilized human limbs. Considering the functions of the leg and the phases of movement, the structure scheme is chosen so that the same actuator performs several functions. This construction partially reduces the load on the person, because the drives of the various links due to their gravity can overturn a person. Using the kinematic structure of the model and the method of adaptive control of the manipulator, as well as replacing some movement parts with plastic material, the authors were successful in reducing the total weight by three times compared with foreign analogues, which is important for a sick person.

The Influence of ground inclination on the energy efficiency of a bipedal walking robot

AbstractOne of the major tasks in developing bipedal walking robots is to improve energy efficiency of their locomotion. In this paper a method for extending this research by considering ground inclinations is introduced. The investigated robot is driven by electric motors in its revolute joints. Robot's gaits for different walking speeds are generated via numerical optimization, minimizing energy consumption during locomotion. Energy efficiency can be increased for differently inclined grounds where the robot utilizes its natural dynamics and gravity.

DESIGN AND DEVELOPMENT OF 6 DOF BIPEDAL ROBOT AND ITS WALKING GAITS1

DESIGN AND DEVELOPMENT OF 6 DOF BIPEDAL ROBOT AND ITS WALKING GAITS1

Development of Bipedal Walking Robot with Flat and Arc-shaped Feet

Development of Bipedal Walking Robot with Flat and Arc-shaped Feet

Simulation and Experimental Walking Pattern Generation for Two Types of Degrees of Freedom Bipedal Locomotion Robot

Humanoids or bipedal robots are other kinds of robots that have legs. The balance of humanoids is the general problem in these types when the other in the support phase and the leg in the swing phase. In this work, the walking pattern generation is studied by MATLAB for two types of degrees of freedom, 10 and 17 degrees of freedom. Besides, the KHR-2HV simulation model is used to simulate the experimental results by Webots. Similarly, Arduino and LOBOT LSC microcontrollers are used to program the bipedal robot. After the several methods for programming the bipedal robot by Arduino microcontroller, LOBOT LSC-32 driver model is the better than PCA 96685 Driver-16 channel servo driver for programming the bipedal walking robot. The results showed that this driver confirms the faster response than the Arduino microcontroller in walking the bipedal robot. The walking pattern generation results showed that the step height for 17 degrees of freedom bipedal robot increases approximately (20%) than 10 degrees of freedom bipedal robot, which decreases the step period by about (7%). Also, the time interval of the double support phase for 17 degrees of freedom bipedal robot increases approximately (11%) with decreases step length approximately (33% on X-axis) and (16% on Z-axis).

EXPERIMENTAL AND THEORETICAL OPTIMAL REGULATOR CONTROL OF BALANCE ZERO MOMENT POINT FOR BIPEDAL ROBOT

In this paper, the optimal control is analyzed to compare the results of the zero moment point of a bipedal walking robot. Seventeen degrees of freedom bipedal walking robot is manufactured of hard Aluminum sheets. The zero moment point is calculated experimentally and theoretically in the single support phase. MATLAB Simulink is used to simulate the results. The experimental results showed that the lower link takes the settling time is (1) sec, the middle link takes settling time (0.9) sec and the upper link takes (1.1) sec to arrive the desired zero moment point for the bipedal walking robot. The minimum performance index in the experimental parts occurs when the optimal feedback control gain is [35.5 30.4 5 -4]. Hence, the minimum performance index in the theoretical part is [35 31 5.2 -4]. The dimensions of the foot area are (12.3cm×6.3cm), 2.3cm thickness, and 32g weight. Also, the approximate balance area in the double support phase equals the area between the feet of the robot.

Transient contact-impact behavior for passive walking of compliant bipedal robots

When bipedal robots walk, the compliance of local contact zone and whole structure usually causes high amplitude contact force and wave propagation excited by impact. The object of this paper is to analyze the transient contact-impact behavior during the walking of compliant bipedal robots (CBR) using a completely flexible body model (CFBM) and obtain its fine contact modes. The applicability of the traditional method based on completely rigid body model (CRBM) to the compliant bipedal robots is also discussed. In CFBM, the structural deformation field and inertial field are discretized by finite element theory, and the penalty function method is adopted to account for the unilateral contact constraint. The dynamic equations, strain and stress equations for the CFBM are derived. The contact forces, impact-induced wave propagation and total mechanical energy of the CBR are calculated. The results show that the relative contact motions between the foot and the slope experience two phases: (1) macroscopic oblique impact phase (duration time is 0.1∼0.2 s) and (2) rolling phase (duration time is 0.6∼0.7 s). Phase 1 consists of a series of fast normal contact-separation switches and tangential stick–slip switches. Phase 2 means there are continuous normal compression and tangential stick. Since the traditional method neglects the first phase, it is not comprehensive for investigating the CBR with impact-induced waves and vibration, especially for the case of lower coefficient of friction (COF) μ. In addition, a ‘dynamic self-locking’ phenomenon is found as μ>0.9, which will lead to the walking unstable.

A Study on Dynamic Motion of Biped Walking Robot

A Study on Dynamic Motion of Biped Walking Robot

Delayed Bilateral Teleoperation of the Speed and Turn Angle of a Bipedal Robot

SUMMARYThis paper proposes a shared control scheme which aims to achieve a stable control of the speed and turn of a bipedal robot during a delayed bilateral teleoperation. The strategy allows to get a delay-dependent damping value that must be injected to assure a bounded response of the hybrid system, while simultaneously, the human operator receives a force feedback that help him to decrease the synchronism error. Furthermore, a test where a human operator handles the walking of a simulated bipedal robot, to follow a curve path in front of varying time delay, is performed and analyzed.

Stabilization of the passive walking dynamics of the compass-gait biped robot by developing the analytical expression of the controlled Poincaré map

The compass-gait biped robot is a two-DoF legged mechanical system that has been known by its passive dynamic walking. This kind of passive biped robot is modeled by an impulsive hybrid nonlinear system that exhibits complex behaviors. This paper is concerned with the stabilization of the passive dynamic walking of the compass-gait biped robot by designing an analytical expression of the controlled Poincare map. The suggested analytical method starts with the time-piecewise linearization of the impulsive system augmented with the controller, around a desired one-periodic passive hybrid limit cycle. By virtue of the first-order Taylor series, we design an explicit expression of the controlled Poincare map. We present also a simplified expression of the controlled Poincare map having a reduced dimension. In order to accomplish our goal concerned with the stabilization of the passive walking dynamics of the compass-gait biped robot, we develop first the linearized Poincare map around the period-1 fixed point of the Poincare map and we adopt a state-feedback control law to stabilize it. Furthermore, in order to enhance the effectiveness and robustness of the stabilization process, we adopt an LMI-based optimization approach by considering the controlled Poincare map to design the feedback gain. In the end of this work, we provide some numerical and graphical simulation results, to show the validity of the designed control law by means of the controlled Poincare map in the stabilization of the passive dynamic walking of the compass-gait biped robot.