Divergent Component Of Motion Research Articles

Balance and Walking Control for Biped Robot Based on Divergent Component of Motion and Contact Force Optimization

Balance and Walking Control for Biped Robot Based on Divergent Component of Motion and Contact Force Optimization

Enhancing speed recovery rapidity in bipedal walking with limited foot area using DCM predictions

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method’s ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm’s effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet.

Enhancement of humanoid robot locomotion on slippery floors using an adaptive controller

AbstractThis paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.

Rider Cooperative Control of Rear-Wheel-Swing Motorcycle Based on Divergent Component of Motion

Rider Cooperative Control of Rear-Wheel-Swing Motorcycle Based on Divergent Component of Motion

Design, Control, and Validation of a Symmetrical Hip and Straight-Legged Vertically-Compliant Bipedal Robot.

This paper presents the development, modeling, and control of L03, an underactuated 3D bipedal robot with symmetrical hips and straight legs. This innovative design requires only five actuators, two for the legs and three for the hips. This paper is divided into three parts: (1) mechanism design and kinematic analysis; (2) trajectory planning for the center of mass and foot landing points based on the Divergent Component of Motion (DCM), enabling lateral and forward walking capabilities for the robot; and (3) gait stability analysis through prototype experiments. The primary focus of this study is to explore the application of underactuated symmetrical designs and determine the number of motors required to achieve omnidirectional movement of a bipedal robot. Our simulation and experimental results demonstrate that L03 achieves simple walking with a stable and consistent gait. Due to its lightweight construction, low leg inertia, and straight-legged design, L03 can achieve ground perception and gentle ground contact without the need for force sensors. Compared to existing bipedal robots, L03 closely adheres to the characteristics of the linear inverted pendulum model, making it an invaluable platform for future algorithm research.

Artificial‐Intelligence‐Powered Lower Limb Assistive Devices: Future of Home Care Technologies

Healthcare systems are burdened by mobility impairments resulting from aging and neurological conditions. One of the recent advances in robotics is lower limb assistive/rehabilitative devices that can make independent living possible. Nonetheless, some limitations need to be addressed before robotics can be used in home‐based applications. This paper describes the current state of the art in intelligent motion planning and control of lower limb assistive devices, which have addressed some of these challenges. Adaptable central pattern generators and the divergent component of motion are introduced as methods for personalized motion planning based on physical human–robot interaction (pHRI). Uncertainty analysis for neural networks is introduced to increase safety in motion planning based on pHRI. For the case that a user cannot apply physical interaction, a reinforcement‐learning‐based approach is introduced to switch between different modes of walking based on the user's input via a push button embedded in a walker. Moreover, a smart walker is introduced as a device that can be synchronized with the lower limb exoskeleton to assist users with their daily activities. Also, a roadmap for future steps that can make lower limb assistive/rehabilitative devices a good fit for home use is introduced.

Kid-size robot humanoid walking with heel-contact and toe-off motion.

Human-like features, like toe-off, heel-strike can enhance the performance of bipedal robots. However, few studies have considered the anthropomorphism of walking planning. Fewer studies have achieved their toe-off, heel-strike gait planning framework in a child-sized humanoid robot platform. This paper presents a human-like walking control framework based on the Divergent Component of Motion (DCM) com planning method that enables a child-sized humanoid robot to walk with a humanoid pattern with a speed of 0.6 s per step a strike of 30 cm. The control framework consists of three parts: the human-like gait generation of the center of mass (CoM) and swings foot trajectory, the dynamic replan in phase switch and the upper body stabilization controller. The dynamic replanning of the CoM and foot trajectory can efficiently decrease the vibration in the step-phase switch. The up-body stabilization controller can reduce the up-body swing in walking and increase the robot's stability while walking. The robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to estimate the current state of the robot and use force-sensitive resistors (FSR) on the robot foot to identify the actual step phase of the robot. None of these solutions is high-cost or difficult to integrate with a child-size robot. Software simulations and walking experiments are using to verify the motion control algorithm. The effectiveness of the pattern generation and the controller can realize more human-like walking styles in a child-size robot are confirmed.

Intelligent Locomotion Planning With Enhanced Postural Stability for Lower-Limb Exoskeletons

In this letter, an integrated control strategy is developed for both locomotion trajectory planning and postural stability, enabling shared autonomy between the human and lower-limb exoskeleton. Divergent component of motion (DCM) analysis was employed previously based on the linear inverted pendulum flywheel (LIPF) model to regulate the position of the center of mass (CoM) for humanoid robots. In this study, a new extended model is investigated for the DCM analysis by replacing the previous LIPF model, which is tailored for multi-degree-of-freedom (DOF) exoskeletons. This new model is designed to be personalized for each specific user's body by relaxing the assumption of having the total CoM at the hip joint in the previous LIPF model. Accordingly, the exoskeleton has the authority to ensure the postural stability and viability of locomotion in this human-robot interaction (HRI) by adjusting the upper body position using a DCM-based hip correction strategy. Integrating adaptive central pattern generators (CPGs), the human has enough authority to modify the gait trajectories in real-time, while the amplitude and frequency of walking are constrained to their feasible ranges. The effectiveness of this intelligent controller for safe and stable locomotion is investigated through experimental studies on a lower-limb exoskeleton.

Instantaneous Capture Input for Balancing the Variable Height Inverted Pendulum

Balancing is a fundamental need for legged robots due to their unstable floating-base nature. Balance control has been thoroughly studied for simple models such as the linear inverted pendulum thanks to the concept of the instantaneous capture point (ICP), yet the constant center of mass height assumption limits the application. This letter explores balancing of the variable-height inverted pendulum (VHIP) model by introducing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instantaneous capture input</i> (ICI), an extension of the ICP based on its key properties. Namely, the ICI can be computed as a function of the state, and when this function is used as the control policy, the ICI is rendered stationary and the system will eventually come to a stop. This characterization induces an analytical region of capturable states for the VHIP, which can be used to conceptually guide where to step. To further address state and control constraints during recovery, we present and theoretically analyze an explicit ICI-based controller with online optimal feedback gains. Simulations demonstrate the validity of our controller for capturability maintenance compared to an approach based on the divergent component of motion.

Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

In order for a humanoid robot to perform loco-manipulation such as moving an object while walking, it is necessary to account for sustained or alternating external forces other than ground-feet reaction, resulting from humanoid-object contact interactions. In this letter, we propose a bipedal control strategy for humanoid loco-manipulation that can cope with such external forces. First, the basic formulas of the bipedal dynamics, i.e., linear inverted pendulum mode and divergent component of motion, are derived, taking into account the effects of external manipulation forces. Then, we propose a pattern generator to plan center of mass trajectories consistent with the reference trajectory of the manipulation forces, and a stabilizer to compensate for the error between desired and actual manipulation forces. The effectiveness of our controller is assessed both in simulation and loco-manipulation experiments with real humanoid robots.

Universal Walking Control Framework of Biped Robot Based on Dynamic Model and Quadratic Programming

Biped robot research has always been a research focus in the field of robot research. Among them, the motion control system, as the core content of the biped robot research, directly determines the stability of the robot walking. Traditional biped robot control methods suffer from low model accuracy, poor dynamic characteristics of motion controllers, and poor motion robustness. In order to improve the walking robustness of the biped robot, this paper solves the problem from three aspects: planning method, mathematical model, and control method, forming a robot motion control framework based on the whole-body dynamics model and quadratic planning. The robot uses divergent component of motion for trajectory planning and introduces the friction cone contact model into the control frame to improve the accuracy of the model. A complete constraint equation system can ensure that the solution of the controller meets the dynamic characteristics of the biped robot. An optimal controller is designed based on the control framework, and starting from the Lyapunov function, the convergence of the optimal controller is proved. Finally, the experimental results show that the method is robust and has certain anti-interference ability.

A Benchmarking of DCM-Based Architectures for Position, Velocity and Torque-Controlled Humanoid Robots

This paper contributes toward the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body quadratic programming (QP) control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372[Formula: see text]m/s, which is the highest walking velocity achieved by the iCub robot.

Study on Walking Strategy of Biped Robot Based on Dynamics of Divergent Component of Motion

In this paper, based on the linear inverted pendulum (LIP) model, the multi-walking of biped robot is analogous to the multi-swing of a three-dimensional inverted pendulum. In terms of the concept of 'divergent component of motion (DCM)', the dynamic equations expressed by using Center of Mass (COM) and DCM are studied. Two DCM closed-loop controllers are designed:one-step DCM terminal invariant disturbance rejection controller and real-time DCM trajectory tracking closed-loop controller. Both controllers can effectively suppress the disturbance, so that the DCM of the actual robot does not diverge, and which is used to plan the COM trajectory of the biped walking process. Based on the COM trajectory and biped end trajectory, the numerical method for solving inverse kinematics of biped robot is studied. The whole set of solving problems from input footprint to output joint angle in biped walking process is completed, and systematize the method of biped gait planning. Finally, combining with a ubiquitous robot model, all the algorithms in this paper are simulated via MATLAB platform. The simulation results verify the effectiveness of the method.

Model Predictive Control for Stable Walking Using the Divergent Component of Motion with Footstep Location and Yaw Adaptation

This paper presents an extension of previous model predictive control (MPC) schemes for dynamic walking to the stabilization of the time-varying divergent component-of-motion (DCM). In order to address the control authority limitations caused by fixed footholds, the step positions and rotations are treated as control inputs, allowing the generation and execution of stable walking motions, both at high speeds and in the face of disturbances. The use of the time-varying DCM allows consideration of height changes on the DCM dynamics, improving the robustness of the controller over varying terrain. Footstep rotation is included to allow for better modeling of the adjustment effects on reachability for stability and navigation of complex environments. This is done by formulating a quadratically constrained mixed-integer quadratic program (MIQCQP), which, when combined with the use of the time-varying DCM to account for the effects of height changes and use of angular momentum, improves the capabilities of MPC strategies for bipedal walking. While the MIQCQP cannot be solved at the desired control frequency, a method for compensating for the DCM dynamics between solves is presented. Simulation results of fast walking over flat ground and navigating varying-height terrain is presented with the ESCHER humanoid. This is combined with experiments that recover from a variety pushes, which demonstrate the effectiveness of this approach.

Contact Force/Torque Control Based on Viscoelastic Model for Stable Bipedal Walking on Indefinite Uneven Terrain

Humanoid robots are being designed to perform tasks currently carried out by human workers in industry, manufacturing, service, and disaster assistance. To this end, the humanoid robot should be able to walk stably across many types of terrain. However, when traversing a complex unknown environment, it is difficult to realize accurate terrain perception immediately through large data collected by the sensor system, leading to a difference between planned foot landing positions and actual foot landing positions. As a result, an unexpected contact force/torque may affect the stability of the robot. This paper adopts active contact perception instead of terrain perception and proposes a contact force/torque control method based on the viscoelastic model to address this problem. In addition, we design a body stability controller based on tracking the trajectories of the virtual repellent point (VRP) and the divergent component of motion (DCM) to restrain the disturbance caused by the unexpected contact force/torque. Simulations and experiments on the BHR-6P humanoid robot platform demonstrate the proposed contact force/torque control method for walking on indefinite uneven terrain. Note to Practitioners —This paper presents a contact force/torque controller based on a viscoelastic model, which unifies contact perception and adaptive reaction. Using the viscoelastic model, we get the relationship between the end position/posture and the first-order differential of the contact force/torque, from which a state equation can be established and used to design a state feedback controller. Combining with body stabilization, we adopt this controller to modify the trajectory of both landing and support feet simultaneously for humanoid robots to realize stable bipedal walking on indefinite uneven terrain, which is validated in both simulations and experiments on the BHR-6P humanoid robot. In addition, this method can be employed for other robots or equipment when the contact force/torque control is needed; e.g., industry manipulators and quadruped robots.

Real-time humanoid whole-body remote control framework for imitating human motion based on kinematic mapping and motion constraints

ABSTRACTIn this paper, we propose a whole-body remote control framework that enables a robot to imitate human motion efficiently. The framework is divided into kinematic mapping and quadratic programming based whole-body inverse kinematics. In the kinematic mapping, the human motion obtained through a data acquisition device is transformed into a reference motion that is suitable for the robot to follow. To address differences in the kinematic configuration and dynamic properties of the robot and human, quadratic programming is used to calculate the joint angles of the robot considering self-collision, joint limits, and dynamic stability. To address dynamic stability, we use constraints based on the divergent component of motion and zero moment point in the linear inverted pendulum model. Simulation using Choreonoid and a locomotion experiment using the HUBO2+ demonstrate the performance of the proposed framework. The proposed framework has the potential to reduce the preview time or offline task computation time found in previous approaches and hence improve the similarity of human and robot motion while maintaining stability.

Extended Three-Dimensional Walking and Skating Motion Generation for Multiple Noncoplanar Contacts With Anisotropic Friction: Application to Walk and Skateboard and Roller Skate

In this letter, we propose a three-dimensional (3-D) walking and skating motion generation method to achieve sequential walking and skating motion with skateboard and roller skate. For generating the sequential stable skating motion using a passive wheel, we must deal with non-coplanar contacts with anisotropic friction. Therefore, we use contact wrench cone (CWC) for considering complex contact states and introduce the novel terminal constraints using the CWC and divergent component of motion, which guarantee the stability of both walking and skating motion in the 3-D center of mass trajectory generation. Applying proposed method, the life-sized humanoid JAXON could successfully walk at 0.6 [m/s] and skate with skateboard and roller skate at 1.0 [m/s].

Convex Properties of Center-of-Mass Trajectories for Locomotion Based on Divergent Component of Motion

This letter presents an in-depth analysis of the convex properties of center-of-mass (CoM) trajectories for legged robot locomotion based on the concept of Divergent Component of Motion (DCM). In particular, we show that the union of all possible trajectories forms a bounded convex set under appropriate boundary conditions. Additionally, we describe in detail our approach of generating closed-form CoM trajectories through piecewise interpolation over a sequence of waypoints and show how to compute the CoM trajectory efficiently through equations given in a matrix form. Applying the convex properties to our trajectory-generation approach, we present an algorithm for computing convex overapproximations of the CoM waypoints. Finally, we provide an example of usage in placing waypoints that lead to feasible CoM trajectories with respect to kinematic and dynamic constraints. The approach is validated with a multi-contact scenario in simulation with the humanoid robot TORO.

The DCM generalized inverse: efficient body-wrench distribution in multi-contact balance control

ABSTRACTA computationally efficient solution of the body- wrench distribution problem for bipeds and multi-legged robots is introduced. The method is based on a weighted generalized inverse, the weights being determined from relationships pertinent to the divergent component of motion (DCM), base-of-support (BoS) geometry, friction constraints and center of pressure allocation. The user (or the robot) specifies appropriate weights only indirectly, by setting the desired contact transition boundaries within the net BoS. It is shown that the proposed weighted generalized inverse ensures body- wrench distribution in a way consistent with both the static and dynamic states. The dependency on the DCM yields an important advantage when the method is applied to reactive balance control in response to unknown disturbances. An admittance-type stabilizer is obtained by setting the reference DCM at the current center of mass position. This stabilizer does not require reference values for the centers of pressures and the contact wrenches. The method is implemented with a whole-body, torque-based balance controller. Its performance is examined through simulations with planar and non-coplanar contacts, during proactive and reactive tasks.

Optimization-Based Whole-Body Control of a Series Elastic Humanoid Robot

As whole-body control approaches begin to enter the mainstream of humanoid robotics research, there is a real need to address the challenges and pitfalls encountered in hardware implementations. This paper presents an optimization-based whole-body control framework enabling compliant locomotion on THOR, a 34 degree of freedom humanoid featuring force-controllable series elastic actuators (SEAs). Given desired momentum rates of change, end-effector accelerations, and joint accelerations from a high-level locomotion controller, joint torque setpoints are computed using an efficient quadratic program (QP) formulation designed to solve the floating-base inverse dynamics (ID). Constraints on the centroidal dynamics, frictional contact forces, and joint position/torque limits ensure admissibility of the optimized joint setpoints. The control approach is supported by an electromechanical design that relies on custom linear SEAs and embedded joint controllers to accurately regulate the internal and external forces computed by the whole-body QP. Push recovery and walking tests conducted using the THOR humanoid validate the effectiveness of the proposed approach. In each case, balancing is achieved using a planning and control approach based on the time-varying divergent component of motion (DCM) implemented for the first time on hardware. We discuss practical considerations that led to the successful implementation of low-impedance whole-body control on our hardware system including the design of the robot’s high-level standing and stepping behaviors and low-level joint-space controllers. The paper concludes with an application of the presented approach for a humanoid firefighting demonstration onboard a decommissioned US Navy ship.