Biped Humanoid Robot Research Articles

Modeling and numerical analysis of Kangaroo lower body based on constrained dynamics of hybrid serial–parallel floating-base systems

This paper presents the modeling and numerical analysis of the Kangaroo lower body prototype, a novel bipedal humanoid robot developed and manufactured by PAL Robotics. Kangaroo features high-power linear electric actuators combined with unique serial–parallel hybrid chains, which allow for the positioning of all the leg actuators near the base of the robot to improve the overall mass distribution. To model and analyze such complex nonlinear mechanisms, we employ a constrained formulation that is extended to account for floating-base systems in contact with the environment. A comparison is made to demonstrate the significant improvements achieved with TALOS, another humanoid bipedal robot designed by PAL Robotics, in terms of equivalent Cartesian inertia at the feet and centroidal angular momentum. Finally, the paper includes numerical experiments conducted through simulation and preliminary tests performed on the actual Kangaroo platform.

Kinematic Modelling of Humanoid Robot Based on Vectorial Approach

The present work is dedicated to the study of the inverse kinematics solution of a humanoid bipedal robot with 30 degrees of freedom using a newly approach based on vectors that generated from the geometric space of the robot. The solutions of the inverse kinematics problem for all joints articulations are carried out through a simple and linear equations. Finally, simulations of results are performed through harmonization of trajectories of end effectors of the robot's members.

Global-Position Tracking Control for Multi-Domain Bipedal Walking with Underactuation

Abstract Accurate control of a humanoid robot's global position (i.e., its three-dimensional position in the world) is critical to the reliable execution of high-risk tasks such as avoiding collision with pedestrians in a crowded environment. This paper introduces a time-based nonlinear control method that achieves accurate global-position tracking (GPT) for multi-domain bipedal walking. Deriving a tracking controller for bipedal robots is challenging due to the highly complex robot dynamics that are time-varying and hybrid, especially for multi-domain walking that involves multiple phases/domains of full actuation, over actuation, and underactuation. To tackle this challenge, we introduce a continuous-phase GPT control law for multi-domain walking, which provably ensures the exponential convergence of the entire error state within the full and over actuation domains and that of the directly regulated error state within the underactuation domain. We then construct sufficient multiple-Lyapunov stability conditions for the hybrid multi-domain tracking error system under the proposed GPT control law. We illustrate the proposed controller design through both three-domain walking with all motors activated and two-domain gait with inactive ankle motors. Simulations of a ROBOTIS OP3 bipedal humanoid robot demonstrate the satisfactory accuracy and convergence rate of the proposed control approach under two different cases of multi-domain walking as well as various walking speeds and desired paths.

DESIGNING THE CONTROL SYSTEM OF THBIP-I HUMANOID ROBOT

This article presents a method for planning the static position of THBIP-I (Tsinghua Biped Humanoid Robot) and a control strategy for its execution. The robot includes a head, torso, two arms, and two legs, with a total of 32 degrees of freedom. This project aims to enable the robot to perform static walking in various environments as an independent humanoid robot. Firstly, the mechanical structure and control architecture of the robot are determined. Secondly, a method for creating a static position based on the optimization of the supporting foot is presented, and a control strategy consisting of a combined controller and sensor feedback regulator is described. Navigation experiments demonstrate the robot's ability to maintain a static position, perform desired directional motions, and ascend/descend stairs. Keywords: humanoid robot, control system, regulator, THBIP-II robot, Humanoid Walker Training model

Research on Vibration Control Technology of Robot Motion Based on Magnetorheological Elastomer.

The vibration and impact of a humanoid bipedal robot during movements such as walking, running and jumping may cause potential damage to the robot’s mechanical joints and electrical systems. In this paper, a composite bidirectional vibration isolator based on magnetorheological elastomer (MRE) is designed for the cushioning and damping of a humanoid bipedal robot under foot contact forces. In addition, the vibration isolation performance of the vibration isolator was tested experimentally, and then, a vibration isolator dynamics model was developed. For the bipedal robot foot impact, based on the vibration isolator model, three vibration reduction control algorithms are simulated, and the results show that the vibration damping effect can reach 85%. Finally, the MRE vibration isolator hardware-in-the-loop-simulation experiment platform based on dSPACE has been built to verify the vibration reduction control effect of the fuzzy PID algorithm. The result shows the vibration amplitude attenuates significantly, and this verifies the effectiveness of the fuzzy PID damping control algorithm.

Promoting Social Engagement With a Multi-Role Dancing Robot for In-Home Autism Care.

This work describes the design of real-time dance-based interaction with a humanoid robot, where the robot seeks to promote physical activity in children by taking on multiple roles as a dance partner. It acts as a leader by initiating dances but can also act as a follower by mimicking a child’s dance movements. Dances in the leader role are produced by a sequence-to-sequence (S2S) Long Short-Term Memory (LSTM) network trained on children’s music videos taken from YouTube. On the other hand, a music orchestration platform is implemented to generate background music in the follower mode as the robot mimics the child’s poses. In doing so, we also incorporated the largely unexplored paradigm of learning-by-teaching by including multiple robot roles that allow the child to both learn from and teach to the robot. Our work is among the first to implement a largely autonomous, real-time full-body dance interaction with a bipedal humanoid robot that also explores the impact of the robot roles on child engagement. Importantly, we also incorporated in our design formal constructs taken from autism therapy, such as the least-to-most prompting hierarchy, reinforcements for positive behaviors, and a time delay to make behavioral observations. We implemented a multimodal child engagement model that encompasses both affective engagement (displayed through eye gaze focus and facial expressions) as well as task engagement (determined by the level of physical activity) to determine child engagement states. We then conducted a virtual exploratory user study to evaluate the impact of mixed robot roles on user engagement and found no statistically significant difference in the children’s engagement in single-role and multiple-role interactions. While the children were observed to respond positively to both robot behaviors, they preferred the music-driven leader role over the movement-driven follower role, a result that can partly be attributed to the virtual nature of the study. Our findings support the utility of such a platform in practicing physical activity but indicate that further research is necessary to fully explore the impact of each robot role.

Lightweight design optimization for legs of bipedal humanoid robot

Lightweight design optimization for legs of bipedal humanoid robot

Hierarchical deep reinforcement learning to drag heavy objects by adult-sized humanoid robot

Most research on robot manipulation focuses on objects that are light enough for the robot to pick them up. However, in our daily life, some objects are too big or too heavy to be picked up or carried, so that dragging them is necessary. Although bipedal humanoid robots have nowadays good mobility on level ground, dragging unfamiliar objects including large and heavy objects on various surfaces is an interesting research area with many applications, which will provide insights into human manipulation and will encourage the development of novel algorithms for robot motion planning and control. This is a challenging problem, not only because of the unknown and potentially variable friction of the foot, but also because the feet of the robot may slip during unbalanced poses. In this paper, we propose a novel hierarchical deep learning algorithm that learns how to drag heavy objects with an adult-sized humanoid robot for the first time. First, we present a Three-layered Convolution Volumetric Network (TCVN) for 3D object classification with point clouds volumetric occupancy grid integration. Second, we propose a lightweight real-time instance segmentation method named Tiny-YOLACT for the detection and classification of the floor surface. Third, we propose a deep Q-learning algorithm to learn the policy control of the Center of Mass of the robot (DQL-COM). The DQL-COM algorithm learning is bootstrapped using the ROS Gazebo simulator. After initial training, we complete training on the THORMANG-Wolf, a 1.4 m tall adult-sized humanoid robot with 27 degrees of freedom and weighing 48 kg, on three distinct types of surfaces. We evaluate the performance of our approach by dragging eight different types of objects (e.g., a small suitcase, a large suitcase, a chair). The extensive experiments (480 times on the real robot) included dragging a heavy object with a mass of 84.6 kg (two times greater than the robot’s weight) and showed remarkable success rates of 92.92% when combined with the force–torque sensors, and 83.75% without force–torque sensors.

A marker registration method to improve joint angles computed by constrained inverse kinematics.

Accurate computation of joint angles from optical marker data using inverse kinematics methods requires that the locations of markers on a model match the locations of experimental markers on participants. Marker registration is the process of positioning the model markers so that they match the locations of the experimental markers. Markers are typically registered using a graphical user interface (GUI), but this method is subjective and may introduce errors and uncertainty to the calculated joint angles and moments. In this investigation, we use OpenSim to isolate and quantify marker registration–based error from other sources of error by analyzing the gait of a bipedal humanoid robot for which segment geometry, mass properties, and joint angles are known. We then propose a marker registration method that is informed by the orientation of anatomical reference frames derived from surface-mounted optical markers as an alternative to user registration using a GUI. The proposed orientation registration method reduced the average root-mean-square error in both joint angles and joint moments by 67% compared to the user registration method, and eliminated variability among users. Our results show that a systematic method for marker registration that reduces subjective user input can make marker registration more accurate and repeatable.

Falling Control for Biped Humanoid Robots Utilizing Capturability Analysis to Determine Landing Position of Upper Limbs

Falling Control for Biped Humanoid Robots Utilizing Capturability Analysis to Determine Landing Position of Upper Limbs

A Machine Learning Approach for Improving the Movement of Humanoid NAO’s Gaits

A humanoid robot’s development requires an incredible combination of interdisciplinary work from engineering to mathematics, software, and machine learning. NAO is a humanoid bipedal robot designed to participate in football competitions against humans by 2050, and speed is crucial for football sports. Therefore, the focus of the paper is on improving NAO speed. This paper is aimed at testing the hypothesis of whether the humanoid NAO walking speed can be improved without changing its physical configuration. The applied research method compares three classification techniques: artificial neural network (ANN), Naïve Bayes, and decision tree to measure and predict NAO’s best walking speed, then select the best method, and enhance it to find the optimal average velocity speed. According to Aldebaran documentation, the real NAO’s robot default walking speed is 9.52 cm/s. The proposed work was initiated by studying NAO hardware platform limitations and selecting Nao’s gait 12 parameters to measure the accuracy metrics implemented in the three classification models design. Five experiments were designed to model and trace the changes for the 12 parameters. The preliminary NAO’s walking datasets open‐source available at GitHub, the NAL, and RoboCup datasheets are implemented. All generated gaits’ parameters for both legs and feet in the experiments were recorded using the Choregraphe software. This dataset was divided into 30% for training and 70% for testing each model. The recorded gaits’ parameters were then fed to the three classification models to measure and predict NAO’s walking best speed. After 500 training cycles for the Naïve Bayes, the decision tree, and ANN, the RapidMiner scored 48.20%, 49.87%, and 55.12%, walking metric speed rate, respectively. Next, the emphasis was on enhancing the ANN model to reach the optimal average velocity walking speed for the real NAO. With 12 attributes, the maximum accuracy metric rate of 65.31% was reached with only four hidden layers in 500 training cycles with a 0.5 learning rate for the best walking learning process, and the ANN model predicted the optimal average velocity speed of 51.08% without stiffness: V1 = 22.62 cm/s, V2 = 40 cm/s, and V = 30 cm/s. Thus, the tested hypothesis holds with the ANN model scoring the highest accuracy rate for predicting NAO’s robot walking state speed by taking both legs to gauge joint 12 parameter values.

Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons

In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners —For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs.

Posture control for humanoid robot on uneven ground and slopes using inertial sensors

We designed a stable gait pattern and posture-control balance system to enable a biped humanoid robot to maintain balance and avoid falling when walking on uneven ground or slopes. In this study, we first examined the problem of gait generation and the balance of a humanoid robot and then proposed a posture-control balance system using the inertial sensors of a gyroscope and accelerometer to sense the tilt angle of the robot according to the environment. To process the data obtained by the sensors, the mean filter was applied to eliminate the noise in the data, and the complementary filter was used to properly combine the data from both the gyroscope and accelerometer. The system further modifies the gait and posture of the robot based on the results obtained through a fuzzy system to attain the angle of balance and stabilization. A robot with an open platform was used to test the implementation of the proposed algorithm, and the experimental results demonstrated that the robot could successfully maintain balance when walking uphill and downhill on uneven surfaces. Moreover, because only one parameter needs to be adjusted when applying the balance-control system, the system can be easily extended to any related humanoid robot.

Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot

This study proposes a real-time sequential sensor fusion based gait pattern controller. During the walking cycle, the sensor data is analyzed and obtained by several inertial measurement units (IMUs) and pressure sensors on the robot. Besides, in order to model the correction values of the walking parameters, this approach applies long short-term memory (LSTM) to build a feedback system for a bipedal humanoid robot. For training the network, four sequential features including two-direction Center of Pressure (CoP) and two-direction acceleration are acquired by using pressure sensors and IMUs on the robot. While these features are mapped to the network inputs, the network output is mapped by the optimal solutions generated using particle swarm optimization (PSO). The proposed LSTM feedback network comprises four layers. The effect of the proposed methodology is first tested in a robot simulation environment and then tested on a real bipedal humanoid robot. The experimental results demonstrate that this methodology can make the humanoid robot perform better than when using a fixed gait. It also provides self-adjustment capability to the robot to enable it to avoid situations like falling.

Fall Protection Framework of Lower Extremity Exoskeleton Walking System Based on Differential Motion Planning

Exoskeleton is a wearable power-assisted supporting device that is used to enhance human motion functions such as walking. This research work is an attempt to provide the paraplegic patients with a more stable human-like hands-free walking function. An exoskeleton model for lower extremities, called a Lower Extremity Exoskeleton Robot (LEE Robot), is developed to feature an automatic posture adjustment and walking balance capability. In order to design a better hands free exoskeleton, the advanced modeling, comprehensive analysis and 3D walking simulations for a humanoid bipedal robot are carried out as a basic research to direct the performance improvement and validate the walking stability and posture optimization. The law of balance is also applied in the analysis of kinematics, statics and dynamics to enhance the new exoskeleton design. Moreover, a conceptual framework is proposed and further developed based on the robot differential motion planning and the zero-moment point (ZMP) stability criterion to realize and test for better fall detection, fall protection and fall avoidance when it is walking. The 3D graphical simulation results demonstrate a success for the new exoskeleton design to offer a better hands free and more stable paraplegic walking.

A Review: Robust Locomotion for Biped Humanoid Robots

One of the most interesting and pressing challenges in the study on biped humanoid robots is to achieve high robustness in locomotion. This paper presents a brief overview of work and methods on robust walking and running for bipedal robots. So far, many robust walking methods have been proposed to reject terrain disturbances and impulsive force disturbances. The applications of the proposed methods to real robots improve the robustness and adaptivity of robots by large margin. Up to now, bipedal robots can traverse unknown terrains with ground variation exceeding 20% of leg length. The height of obstacles increases more than threefold compared to decades ago. With regards to unexpected external force, bipedal robots can recover the balance from sudden push not only at stationary state, but also during the walk. On the other hand, the biped running is underdeveloped compared to the robust walking. Still the highest running speed is less than 3.0 m/s, not to mention the poor robustness to large disturbances.

The influence of sagittal trunk lean on uneven running mechanics.

The role of trunk orientation during uneven running is not well understood. This study compared the running mechanics during the approach step to and the step down for a 10 cm expected drop, positioned halfway through a 15 m runway, with that of the level step in 12 participants at a speed of 3.5 m s-1 while maintaining self-selected (17.7±4.2 deg; mean±s.d.), posterior (1.8±7.4 deg) and anterior (26.6±5.6 deg) trunk leans from the vertical. Our findings reveal that the global (i.e. the spring-mass model dynamics and centre-of-mass height) and local (i.e. knee and ankle kinematics and kinetics) biomechanical adjustments during uneven running are specific to the step nature and trunk posture. Unlike the anterior-leaning posture, running with a posterior trunk lean is characterized by increases in leg angle, leg compression, knee flexion angle and moment, resulting in a stiffer knee and a more compliant spring-leg compared with the self-selected condition. In the approach step versus the level step, reductions in leg length and stiffness through the ankle stiffness yield lower leg force and centre-of-mass position. Contrariwise, significant increases in leg length, angle and force, and ankle moment, reflect in a higher centre-of-mass position during the step down. Plus, ankle stiffness significantly decreases, owing to a substantially increased leg compression. Overall, the step down appears to be dominated by centre-of-mass height changes, regardless of having a trunk lean. Observed adjustments during uneven running can be attributed to anticipation of changes to running posture and height. These findings highlight the role of trunk posture in human perturbed locomotion relevant for the design and development of exoskeleton or humanoid bipedal robots.

Embedded Walking Algorithm for Biped Humanoid Robot with 17 Degrees-of-Freedom

The realization of the control algorithm consists of two main stages: planning of a joint coordination sequence and implementation of an embedded walking algorithm in the Arduino Mega 2560 hardware platform. The joint coordination sequence consists of 13 steps in order for the humanoid robot to move forward without falling. The embedded walking algorithm is realized around the operating points of the servo motors. The software is developed in Arduino IDE in C language.

When joggers meet robots: the past, present, and future of research on humanoid robots

Spawned by fast-paced progress in new materials and integrate circuit technology, the past two decades have witnessed tremendous development of humanoid robots for both scientific and commercial purposes, e.g. emergency response and daily life assistant. At the root of this trend are the increasing research interests and cooperation opportunities across different laboratories and countries. The application-driven requirements of high effectiveness and reliability of humanoid robots led intensive research and development in humanoid locomotion and control theories. In spite of the progress in the area, challenges such as unnatural locomotion control, inefficient multi-motion planning, and relatively slow disturbances recovery set further requirements for the next generation of humanoid robots. Therefore, the purpose of this work is to review the current development of highly representative bipedal humanoid robots and discuss the potential to move the ideas and models forward from laboratory settings into the real world. To this end, we also review the current clinical understanding of the walking and running dynamics to make the robot more human-like.

Development of the Legs of a Small Biped Humanoid Robot

Development of the Legs of a Small Biped Humanoid Robot