Cascade position- torque control strategy based on function approximation technique (FAT) for flexible joint robots

This work deals with control of rigid link robotic manipulators provided with flexible joints. Due to presence of flexible joint dynamics, additional degrees of freedom and underactuation are developed that would complicate the control design. Besides, model uncertainties, unmodeled dynamics and disturbances should be considered in robot modeling and control. Therefore, this paper proposes a cascade position-torque control strategy based on function approximation technique (FAT). The key idea is to design two nested loops: 1) an outer position control loop for tracking reference trajectory, and 2) an inner joint torque control loop to track the desired joint torque resulted from the outer position loop. The torque control loop makes the robot system more adaptable and compliant for sudden disturbances. It increases the perception capability for the target robot mechanisms. Adaptive approximation control (AAC) is used as a strong tool for dealing with time-varying uncertain parameters and disturbances. A sliding mode term is easily integrated with control law structure; however, a constraint on feedback gains are established for compensating modeling (approximation) error. The proposed control architecture can be easily used for high degrees of freedom robotic system due to the decentralized behavior of the AAC. A two-link manipulator is used for simulation experiments.The simulated robot is commanded to move from rest to desired step references considering three cases depending on the selected value of the sliding mode time constant. It is shown that selection of a large time constant parameter related to the position loop leads to slow response. Besides, one of the inherent issues associated with the inner torque control is the presence of derivative of desired joint torque that makes the input control abruptly jumping at the beginning of the dynamic response. To end this, an approximation for derivative term of the desired joint torque is established using a low-pass filter with a time constant selected carefully such that a feasible dynamic response is ensured.The results show the effectiveness of the proposed controller.

Cascade position- torque control strategy based on function approximation technique (FAT) for flexible joint robots

This work deals with control of rigid link robotic manipulators provided with flexible joints. Due to presence of flexible joint dynamics, additional degrees of freedom and underactuation are developed that would complicate the control design. Besides, model uncertainties, unmodeled dynamics and disturbances should be considered in robot modeling and control. Therefore, this paper proposes a cascade position-torque control strategy based on function approximation technique (FAT). The key idea is to design two nested loops: 1) an outer position control loop for tracking reference trajectory, and 2) an inner joint torque control loop to track the desired joint torque resulted from the outer position loop. The torque control loop makes the robot system more adaptable and compliant for sudden disturbances. It increases the perception capability for the target robot mechanisms. Adaptive approximation control (AAC) is used as a strong tool for dealing with time-varying uncertain parameters and disturbances. A sliding mode term is easily integrated with control law structure; however, a constraint on feedback gains are established for compensating modeling (approximation) error. The proposed control architecture can be easily used for high degrees of freedom robotic system due to the decentralized behavior of the AAC. A two-link manipulator is used for simulation experiments.The simulated robot is commanded to move from rest to desired step references considering three cases depending on the selected value of the sliding mode time constant. It is shown that selection of a large time constant parameter related to the position loop leads to slow response. Besides, one of the inherent issues associated with the inner torque control is the presence of derivative of desired joint torque that makes the input control abruptly jumping at the beginning of the dynamic response. To end this, an approximation for derivative term of the desired joint torque is established using a low-pass filter with a time constant selected carefully such that a feasible dynamic response is ensured.The results show the effectiveness of the proposed controller.

A Custom-Made Lower Limb Dynamometer for Assessing Ankle Joint Torque in Humans: Calibration and Measurement Procedures

Custom-made dynamometry was shown to objectively analyze human muscle strength around the ankle joint with accuracy, easy portability and low costs. This paper describes the full method of calibration and measurement setup and the measurement procedure when capturing ankle torque for establishing reliability of a portable custom-built electronic dynamometer. After considering the load cell offset voltage, the pivotal position was determined, and calibration with loads followed. Linear regression was used for calculating the proportionality constant between torque and measured voltage. Digital means were used for data collection and processing. Four healthy consenting participants were enrolled in the study. Three consecutive maximum voluntary isometric contractions of five seconds each were registered for both feet during plantar flexion/dorsiflexion, and ankle torque was then calculated for three ankle inclinations. A calibration procedure resulted, comprising determination of the pivotal axis and pedal constant. Using the obtained data, a measurement procedure was proposed. Obtained contraction time graphs led to easier filtering of the results. When calculating the interclass correlation, the portable apparatus demonstrated to be reliable when measuring ankle torque. When a custom-made dynamometer was used for capturing ankle torque, accuracy of the method was assured by a rigorous calibration and measurement protocol elaboration.

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

Whole-body kinematic and dynamic modeling for quadruped robot under different gaits and mechanism topologies

Dynamic locomotion plays a crucial role for legged robots to fulfill tasks in unstructured environments. This paper proposes whole-body kinematic and dynamic modeling method s based on screw theory for a quadruped robot using different gaits and mechanism topologies. Unlike simplified models such as centroid or inverse pendulum models, the methods proposed here can handle 10-dimensional mass and inertia for each part. The only simplification is that foot contact models are treated as spherical joints. Models of three different mechanism topologies are formulated: (1) Standing phase: a system consisting of one end-effector, the body, and four limbs, the legs; (2) Walking phase: a system consisting of one or two lifting legs (depending on the chosen gait), two or three supporting legs; (3) Floating phase: a system in which all legs detach from the ground. Control strategies based on our models are also introduced, which includes walk and trot gait plans. In our control system, two additional types of information are provided: (1) contacting forces are given by force sensors installed under feet; (2) body poses are determined by an inertial measurement unit (IMU). Combined with the sensor data and calibrated mass, inertia, and friction, the joint torque can be estimated accurately in simulation and experiment. Our prototype, the “XiLing” robot, is built to verify the methods proposed in this paper, and the results show that the models can be solved quickly and leads to steady locomotions.

Characteristics of inhomogeneous lower extremity growth and development in early childhood: a cross-sectional study

Background Early childhood is a transferring stage between the two accelerated growth periods (infant and adolescent). Body dimensions are related to physical growth and development. The purpose of this study was to investigate physical growth in terms of anthropometry, muscle growth of the lower extremity, and functional development over early childhood. Methods A cross-sectional study was carried out on 29 preschool children (PS: 3–5 years), 21 school children (SC: 6–8 years), and 22 adults (AD: 20–35 years). Lower extremity characteristics (segmental dimensions, muscle and adipose tissue thicknesses of the thigh and lower leg), and voluntary joint torque (knee and ankle) were measured. Correlations between parameters and group comparisons were performed. Results All the parameters except for body mass index (BMI) and subcutaneous adipose tissue thickness were correlated with age for PS and SC combined (r = 0.479–0.920, p < 0.01). Relative thigh and shank lengths to body height were greatest in AD and smallest in PS (p < 0.05) but the relative foot dimensions were significantly larger in PS and SC than in AD (p < 0.05). Relative subcutaneous adipose tissue thickness was largest in PS and lowest in AD. Muscle thickness and the muscle volume measure (estimated from muscle thickness and limb length) were significantly larger in older age groups (p < 0.05). All groups showed comparable muscle thickness when normalized to limb length. Joint torque normalized to estimated muscle volume was greatest for AD, followed by SC and PS (p < 0.05). Conclusions Relative lower extremity lengths increase with age, except for the foot dimensions. Muscle size increases with age in proportion to the limb length, while relative adiposity decreases. Torque-producing capacity is highly variable in children and rapidly develops toward adulthood. This cross-sectional study suggests that children are not a small scale version of adults, neither morphologically nor functionally.

Just noticeable differences for elbow joint torque feedback

Joint torque feedback is a new and promising means of kinesthetic feedback imposed by a wearable device. The torque feedback provides the wearer temporal and spatial information during a motion task. Nevertheless, little research has been conducted on quantifying the psychophysical parameters of how well humans can perceive external torques under various joint conditions. This study aims to investigate the just noticeable difference (JND) perceptual ability of the elbow joint to joint torques. The paper focuses on the ability of two primary joint proprioceptors, the Golgi-tendon organ (GTO) and muscle spindle (MS), to detect elbow torques, since touch and pressure sensors were masked. We studied 14 subjects while the arm was isometrically contracted (static condition) and was moving at a constant speed (dynamic condition). In total there were 10 joint conditions investigated, which varied the direction of the arm’s movement and the preload direction as well as torque direction. The JND torques under static conditions ranged from 0.097 Nm with no preload to 0.197 Nm with a preload of 1.28 Nm. The maximum dynamic JND torques were 0.799 Nm and 0.428 Nm, when the arm was flexing and extending at 213 degrees per second, respectively.

End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach.