Ground reaction force sensor fault detection and recovery method based on virtual force sensor for walking biped robots

This paper presents a novel method for ground force sensor faults detection and faulty signal reconstruction using Virtual force Sensor (VFS) for slow walking bipeds. The design structure of the VFS consists of two steps, the total ground reaction force (GRF) and its location estimation for each leg based on the center of mass (CoM) position, the leg kinematics, and the IMU readings is carried on in the first step. In the second step, the optimal estimation of the distributed reaction forces at the contact points in the feet sole of walking biped is carried on. For the optimal estimation, a constraint model is obtained for the distributed reaction forces at the contact points and the quadratic programming optimization method is used to solve for the GRF. The output of the VFS is used for fault detection and recovery. A faulty signal model is formed to detect the faults based on a threshold, and recover the signal using the VFS outputs. The sensor offset, drift, and frozen output faults are studied and tested. The proposed method detects and estimates the faults and recovers the faulty signal smoothly. The validity of the proposed estimation method was confirmed by simulations on 3D dynamics model of the humanoid robot SURALP while walking. The results are promising and prove themselves well in all of the studied fault cases

Simple virtual slip force sensor for walking biped robots

This paper presents a novel simple Virtual Slip Force Sensor (VSFS) for a walking biped. Bipeds walking stability is critical and they tend to lose it easily in real environments. Among the significant aspects that affect the stability is the availability of the required friction force which is necessary for the robot not to slip. In this paper we propose the use of the virtual sensor to detect the slip force. The design structure of the VSFS consists of two steps, in the first step it utilizes the measured acceleration of the center of mass (CoM) and the ZMP signals in the simple linear inverted pendulum model (LIPM) to estimate the position of the CoM, and in the second step the Newton law is employed to find the total ground reaction force (GRF) for each leg based on the position of CoM. Then both the estimated force and the measured force from the sensors assembled at the foot are used to detect the slip force. The validity of the proposed estimation method was confirmed by simulations on 3D dynamics model of the humanoid robot SURALP while walking. The results are promising and prove themselves well

Experimental verification of an orientation estimation technique for autonomous robotic platforms

Research on autonomous platforms ranging from unmanned aerial and underwater vehicles to wheeled, tracked and legged machines enlarges the application boundaries of robotic systems. The control of these platforms, however, is a challenging task which requires the availability or estimation of various feedback variables. Orientation information of the autonomous platform is vital for robotic control. A variety of approaches and instruments are reported in the literature for orientation estimation with inertial sensors. Many approaches are based on Kalman filters. The use of Extended Kalman filters (EKF), Unscented Kalman filters and complimentary Kalman filters are proposed too. This thesis concentrates on the estimation of orientation from measurements provided by inertial sensors. The use of three-axial linear accelerometers and rate gyros is considered. The sequential use of two estimators is proposed for orientation estimation. The first one is a Kalman Filter and it is employed for the gravity estimation mainly based on acceleration readings. The second estimator has the structure of an Extended Kalman Filter which uses the gravity estimate generated by the first estimator and rate gyro readings for the orientation estimation. Orientation is estimated in a multivariable fashion, without the simplifying assumption of decoupling between onedimensional rotations about the three gyroscope axes. Therefore quite large angles of rotation can be handled accurately. The presented approach uses a quaternion representation which avoids representation singularities common with orientation descriptors like Euler angles. The computational efficiency is improved by the quaternion representation too. In order to test the estimation methods, experimental studies are carried out. A three-degrees-of-freedom robot is designed and built. The accelerometer and gyroscope unit is mounted at the tool end of this manipulator which generated test motion in the experiments. In order to create a basis for comparison, robot joint encoder data is used and actual rotation matrices during the motion are computed. The experiments indicate that the proposed technique delivers reliable orientation estimates in a large range of rotation angles and motion frequencies

Joint friction estimation and slip prediction of biped walking robots

Friction is a nonlinear and complex phenomenon. It is unwanted at the biped joints since it deteriorates the robot’s walking performance in terms of speed and dynamic behavior. On the other hand, it is desired and required between the biped feet and the walking surface to facilitate locomotion. Further, friction forces between the feet and the ground determine the maximum acceleration and deceleration that the robot can afford without foot slip. Although several friction models are developed, there is no exact model that represents the friction behavior. This is why online friction estimation and compensation enter the picture. However, when online model-free estimation is difficult, a model-based method of online identification can prove useful. This thesis proposes a new approach for the joint friction estimation and slip prediction of walking biped robots. The joint friction estimation approach is based on the combination of a measurementbased strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, it utilizes the force and acceleration measurements in a reduced dynamical model of the biped. The latter adopts a friction model to represent the joint friction when the leg is swinging. The model parameters are identified adaptively using the estimated online friction whenever the foot is in contact. Then the estimated joint friction contributes to joint torque control signals to improve the control performance. The slip prediction is a model-free friction-behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed estimation approaches are validated by experiments on SURALP (Sabanci University Robotics Research Laboratory Platform) and simulations on its model. The results demonstrate the effectiveness of these methods

An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

Kalman filter (KF) is used extensively for state estimation. Among its requirements are the process and observation noise covariances which are unknown or partially known in real life applications. Biased initializations of the covariances result in performance degradation of KF or divergence. Therefore, an extensive research is carried on to improve its performance however, relying on a moving window, heavy computations, and the availability of the exact model are the fundamental problems in most of the proposed techniques. In this paper, we are using the idea of the recursive estimation of KF to propose two recursive updating rules for the process and observation covariances respectively designed based on the covariance matching principles. Each rule is a tuned scaled version of the previous covariance in addition to a tuned correction term derived based on the most recent available data. The proposed adaptive Kalman filter AKF avoided the aforementioned problems and proved itself to have an improved performance over the conventional KF. The results show that the AKF estimates are more accurate, have less noise and more stable against biased covariance initializations

Reduced filtered dynamic model for joint friction estimation of walking bipeds

The present paper presents a novel method for estimating the joint friction of walking bipeds. It combines a measurement-based method with an adaptive model-based method to estimate the joint friction. The former is used when the feet is in contact, while the latter is used when the leg is swinging. The measurements are the feet forces and the readings of an inertial measure unit located at the biped body. The measurement-based method utilizes these measurements into a reduced filtered dynamic model of the biped to obtain an online estimated filtered version of the joint friction. Once the foot swings, a friction model is adopted to represent the joint friction behavior. The model parameters are adaptively identified using the online estimated filtered friction whenever the foot is in contact. The results are validated using full-dynamics of 12-DOF biped model and show a dramatic tracking of the estimated friction to the true one

Center of mass states and disturbance estimation for a walking biped

An on-line assessment of the balance of the robot requires information of the state variables of the robot dynamics and measurement data about the environmental interaction forces. However, modeling errors, external forces and hard to measure states pose difficulties to the control systems. This paper presents a method of using the motion information to estimate the center of mass (CoM) states and the disturbance of walking humanoid robot. The motion is acquired from the inertial measurement unit (IMU) and forward kinematics only. Kalman filter and disturbance observer are employed, Kalman filter is used for the states and disturbance estimation, and the disturbance observer is used to decompose the disturbance into modeling error and acceleration error based on the frequency band. The disturbance is modeled mathematically in terms of previous CoM and Zero moment point (ZMP) states rather than augmenting it in the system states. The ZMP is estimated using the quadratic programming method to solve the constraint dynamic equations of the humanoid robot in translational motion. A biped robot model of 12-degrees-of-freedom (DOF) is used in the full-dynamics 3-D simulations for the estimation validation. The results indicate that the presented estimation method is successful and promising

An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

Kalman filter (KF) is used extensively for state estimation. Among its requirements are the process and observation noise covariances which are unknown or partially known in real life applications. Biased initializations of the covariances result in performance degradation of KF or divergence. Therefore, an extensive research is carried on to improve its performance however, relying on a moving window, heavy computations, and the availability of the exact model are the fundamental problems in most of the proposed techniques. In this paper, we are using the idea of the recursive estimation of KF to propose two recursive updating rules for the process and observation covariances respectively designed based on the covariance matching principles. Each rule is a tuned scaled version of the previous covariance in addition to a tuned correction term derived based on the most recent available data. The proposed adaptive Kalman filter AKF avoided the aforementioned problems and proved itself to have an improved performance over the conventional KF. The results show that the AKF estimates are more accurate, have less noise and more stable against biased covariance initializations

Joint friction estimation for walking bipeds

This paper proposed a new approach for the joint friction estimation of non-slipping walking biped robots. The proposed approach is based on the combination of a measurement-based strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, while the latter adopts a friction model to represent the joint friction when the leg is swinging. The measurement-based strategy utilizes the measured ground reaction forces and the readings of an inertial measurement unit (IMU) located at the robot body. Based on these measurements, the joint angular accelerations and the body attitude and velocity are estimated. The aforementioned measurements and estimates are used in a reduced dynamical model of the biped. However, when the leg is swinging, this strategy is inapplicable. Therefore, a friction model is adopted. Its parameters are identified adaptively using the estimated online friction whenever the foot is in contact. The estimated joint friction is used in the feedback torque control signal. The proposed approach is validated using the full-dynamics of 12-DOF biped model. By using this approach, the robot center of mass position error is reduced by 10% which demonstrates the effectiveness of this approach

Joint sensor fault detection and recovery based on virtual sensor for walking legged robots

This paper presents a novel method for joint sensor faults detection and faulty signal reconstruction. It uses the Virtual Joint Sensor (VJS). The model structure of the VJS consists of two interconnected models: The simple Linear Inverted Pendulum Model (LIPM) and the robot leg kinematics model (LKM). Kalman filter based on LIPM estimates the Center of Mass (CoM) position of the biped. The LKM uses the estimated CoM position to calculate the joints angles. A faulty signal model is formed to detect the faults, based on an adaptive threshold, and recovers the signal using the VJS outputs. The sensor abrupt, incipient, and frozen output faults are studied and tested. The validity of the proposed method was confirmed by simulations on 3D dynamics model of the humanoid robot SURALP while walking on a flat terrain