WALK-MAN: A High-Performance Humanoid Platform for Realistic Environments

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

Design and Control for WLR-3P: A Hydraulic Wheel-Legged Robot

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex terrain. The hybrid wheel-legged robot (WLR-3P, prototype of the third-generation hydraulic wheel-legged robot) has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough terrains. In this paper, 3 design requirements are proposed to improve the mobility and environmental adaptability of the robot. To meet these 3 requirements, 2 design principles for each requirement are put forward. First, for light weight and low inertia with high stiffness, 3-dimensional printing technology and lightweight material are adopted. Second, the integrated hydraulically driven unit is used for high power density and fast response actuation. Third, the micro-hydraulic power unit achieves power autonomy, adopting the hoseless design to strengthen the reliability of the hydraulic system. What is more, the control system including hierarchical distributed electrical system and control strategy is presented. The mobility and adaptability of WLR-3P are demonstrated with a series of experiments. Finally, the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.

State observer-based model reference adaptive balance control for one-leg stance

Purpose

Humanoid robots have been utilized in many fields such as medical, construction, and disaster response. While humanoid robots nowadays can achieve great capabilities, the one-leg balancing task still poses a challenging problem. This paper aims to propose a novel approach to solve the problem.

Design/methodology/approach

To aid the balance of one leg in humanoid robot, an external balance mechanism is inserted to the back of the humanoid robot. First, a dynamic model of the humanoid robot with balance mechanism and its simplified model are introduced. Second, a backstepping-based control method is utilized to build the proposed controller for one-leg stance system through two steps. For the first step, a minimum observer-based controller with a virtual control input is used to control the first sub-system reaching the desired reference input. For the second step, a virtual control input is considered as a reference input of a second sub-system, then a model reference adaptive controller (MRAC) is employed to control the second sub-system reaching the virtual control input in presence of uncertainties. By using the external balance mechanism, the sideway balancing task is separated from normal walking function. Furthermore, the utilization of the balance mechanism ensures the humanoid robot's hip adduction does not exceed the threshold of a human when walking. Finally, a simulation study is carried out to evaluate the effectiveness of the proposed method.

Findings

This paper proposes a model reference adaptive control using state observer for balancing one leg of humanoid robot in stance phase that extends our previous research (Tran et al., 2021).

Originality/value

The main research contents have been introduced.

Discussion on the Stiffness of the Drive Chain in the Legs of Biped Robots

Biped robots’ locomotion is realized by driving the joint motion via a drive chain. Therefore, the stiffness of the drive chain is an important factor that affects the drive performance and can influence the locomotion behavior of the biped robot. This work focused on the influence of the stiffness of the leg’s drive chain using a mass-spring model based on the biped robot AIRO built in Zhejiang Lab. Methods for determination of the parameters in the proposed model were presented, including the use of ANSYS Workbench to determine the stiffness parameters and the determination of the inertia parameters by dynamic modelling of the biped robot. Simulation results show that special attention should be paid to the stiffness of the drive train of the leg when designing a biped robot to ensure the walking capability of the robot. Using the model proposed in this work, relations between the executed accuracy of the joint trajectories and the stiffness can be analyzed; after that, the stiffness parameters can be optimized. In addition, simulation results also showed that attention should be paid to manufacturing tolerances to ensure the symmetry of the legs of the bipedal robot in order to reduce the vibration of the robot body. Experiments were conducted on AIRO for validating the proposed model and the simulation analysis.

Development of a Design Methodology for Cloud Distributed Control Systems of Mobile Robots

This article addresses the problem of cloud distributed control systems development for mobile robots. The authors emphasize the lack of a design methodology to guide the process of the development in accordance with specific technical and economic requirements for the robot. On the analysis of various robots architectures, the set of the nine most significant parameters are identified to direct the development stage by stage. Based on those parameters, the design methodology is proposed to build a scalable three-level cloud distributed control system for a robot. The application of the methodology is demonstrated on the example of AnyWalker open source robotics platform. The developed methodology is also applied to two other walking robots illustrated in the article.

Autonomous Obstacle Crossing Strategies for the Hybrid Wheeled-Legged Robot Centauro

The development of autonomous legged/wheeled robots with the ability to navigate and execute tasks in unstructured environments is a well-known research challenge. In this work we introduce a methodology that permits a hybrid legged/wheeled platform to realize terrain traversing functionalities that are adaptable, extendable and can be autonomously selected and regulated based on the geometry of the perceived ground and associated obstacles. The proposed methodology makes use of a set of terrain traversing primitive behaviors that are used to perform driving, stepping on, down and over and can be adapted, based on the ground and obstacle geometry and dimensions. The terrain geometrical properties are first obtained by a perception module, which makes use of point cloud data coming from the LiDAR sensor to segment the terrain in front of the robot, identifying possible gaps or obstacles on the ground. Using these parameters the selection and adaption of the most appropriate traversing behavior is made in an autonomous manner. Traversing behaviors can be also serialized in a different order to synthesise more complex terrain crossing plans over paths of diverse geometry. Furthermore, the proposed methodology is easily extendable by incorporating additional primitive traversing behaviors into the robot mobility framework and in such a way more complex terrain negotiation capabilities can be eventually realized in an add-on fashion. The pipeline of the above methodology was initially implemented and validated on a Gazebo simulation environment. It was then ported and verified on the CENTAURO robot enabling the robot to successfully negotiate terrains of diverse geometry and size using the terrain traversing primitives.

Energy-Efficient Bipedal Walking: From Single-Mass Model to Three-Mass Model

The work aims to realize energy-efficient bipedal walking by employing the three-mass inverted pendulum model (3MIPM) and compare its energy performance with linear inverted pendulum model (LIPM). To do this, a general optimal index on center of mass (CoM) acceleration is first derived for energetic cost evaluation. After defining the equivalent zero moment point (ZMP) motion, an unconstrained optimization approach for CoM generation is extended for 3MIPM, which can track different ZMP references and address the height variation as well. To make use of the allowable ZMP movement, a constrained optimization method is also employed, contributing to lower energetic cost. Simulation and hardware experiments on a humanoid robot demonstrate that the 3MIPM could achieve higher energy efficiency.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Design of JET Humanoid Robot with Compliant Modular Actuators for Industrial and Service Applications

This paper presents the development of the JET humanoid robot, which is based on the existing THORMANG platform developed in 2015. Application in the industrial and service fields was targeted, and three design concepts were determined for the humanoid robot. First, low stiffness of the actuator modules was utilized for compliance with external environments. Second, to maximize the robot whole-body motion capability, the overall height was increased. However, the weight was reduced to satisfy power requirements. The workspace was also increased to enable various postures, by increasing the range of motion of each joint and extending the links. Compared to the original THORMANG platform, the lower limb length increased by approximately 20%, and the hip range of motion increased by 39.3%. Third, the maintenance process was simplified through modularization of the electronics and frame design for improved accessibility. Several experiments, including stair climbing and egress from a car, were performed to verify that the JET humanoid robot performance enhancements reflected the design concepts.

Whole-Body Compliant Motion by Sensor Integration of an EHA-Driven Humanoid Hydra

Joints’ backdrivability is desired for robots that perform tasks contacting the environment, in addition to the high torque and fast response property. The electro-hydrostatic actuator (EHA) is an approach to realize force-sensitive robots. To experimentally confirm the performance of a biped robot driven by EHAs, we developed the fully electro-hydrostatically driven humanoid robot Hydra. In this paper, we evaluate the whole-body control performance realized by integrating encoders, pressure sensors, and IMU through a high-speed communication bus to the distributed whole-body control system. We report the first example of bipedal locomotion by an EHA-driven robot in both position-controlled and torque-controlled approaches. The robot could keep the balance even when the ground condition was changing impulsively and utilize its high joint backdrivability to absorb a disturbance by the null space compliance. We also report practical challenges in implementing compliant control in real hardware with limitations in parameter accuracy, torque, and response. We experimentally confirmed that the resolved viscoelasticity control (RVC), which has indirect feedback of operational space tasks by projecting the operational space feedback gain to the joint space one, was effective to tune a proper gain to stabilize the center-of-mass motion while avoiding joint-level oscillation invoked by the control bandwidth limitation. The attached multimedia file includes the video of all experiments presented in the paper.

Parallel Architectures for Humanoid Robots

The structure of humanoid robots can be inspired to human anatomy and operation with open challenges in mechanical performance that can be achieved by using parallel kinematic mechanisms. Parallel mechanisms can be identified in human anatomy with operations that can be used for designing parallel mechanisms in the structure of humanoid robots. Design issues are outlined as requirements and performance for parallel mechanisms in humanoid structures. The example of LARMbot humanoid design is presented as from direct authors’ experience to show an example of the feasibility and efficiency of using parallel mechanisms in humanoid structures. This work is an extension of a paper presented at ISRM 2019 conference (International Symposium on Robotics and Mechatronics).

Eye Gaze Based 3D Triangulation for Robotic Bionic Eyes

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An active binocular vision system with non-fixed cameras needs to calibrate the stereo extrinsic parameters online to perform 3D triangulation. However, the accuracy of stereo extrinsic parameters and disparity have a significant impact on 3D triangulation precision. We propose a novel eye gaze based 3D triangulation method that does not use stereo extrinsic parameters directly in order to reduce the impact. Instead, we drive both cameras to gaze at a 3D spatial point P at the optical center through visual servoing. Subsequently, we can obtain the 3D coordinates of P through the intersection of the two optical axes of both cameras. We have performed experiments to compare with previous disparity based work, named the integrated two-pose calibration (ITPC) method, using our robotic bionic eyes. The experiments show that our method achieves comparable results with ITPC.

A method for autonomous robotic manipulation through exploratory interactions with uncertain environments

Expanding robot autonomy can deliver functional flexibility and enable fast deployment of robots in challenging and unstructured environments. In this direction, significant advances have been recently made in visual-perception driven autonomy, which is mainly due to the availability of rich sensory data-sets. However, current robots’ physical interaction autonomy levels still remain at a basic level. Towards providing a systematic approach to this problem, this paper presents a new context-aware and adaptive method that allows a robotic platform to interact with unknown environments. In particular, a multi-axes self-tuning impedance controller is introduced to regulate quasi-static parameters of the robot based on previous experience in interacting with similar environments and the real-time sensory data. The proposed method is also capable of differentiating internal and external disruptions, and responding to them accordingly and appropriately. An agricultural experiment with different deformable material is presented to validate robot interaction autonomy improvements, and the capability of the proposed methodology in detecting and responding to unexpected events (e.g., faults).

Exploiting the intrinsic deformation of a prosthetic foot to estimate the center of pressure and ground reaction force

The properties of the foot deployed in a bipedal robot that targets the rendering of a human-like dynamic gait are crucial. Firstly, it has to implement a set of mechanical mechanisms/properties that improve the efficiency of the locomotion. Secondly, it has to integrate a sensory system that captures the interaction with the ground with suitable precision. Both systems-the mechanical and the sensory system-have to be integrated as tightly as possible to keep the overall dimensions and weight low. Being the most distal element of the leg, especially the latter is crucial for favorable leg dynamics. Regarding the structural properties, a modern prosthetic foot poses a good solution and has hence been adopted in various bipeds. Their elaborated structures-mostly made from carbon fiber composites-are designed to imitate the mechanisms of the anthropomorphic counterpart. The following presents a concept to estimate the ground interaction based on the intrinsic deformation of a commercially available prosthesis. To measure the deformation, strain gauges are applied to its main structural elements. Using this information, the center of pressure and the normal force acting on it are estimated. The performance of two approaches-linear regression and neural networks-is presented and compared. Finally, the accuracy of the strain-based estimation is evaluated in two experiments and compared to a conventional force/torque sensor (FTS)-based system and a pressure insole. While the presented work is initially motivated by robotics research, it might as well be transferred to the design of a modern actively actuated prosthesis.

Peg-in-Hole Assembly Based on Six-Legged Robots with Visual Detecting and Force Sensing

Manipulators with multi degree-of-freedom (DOF) are widely used for the peg-in-hole task. Compared with manipulators, six-legged robots have better mobility performance apart from completing operational tasks. However, there are nearly no previous studies of six-legged robots performing the peg-in-hole task. In this article, a peg-in-hole approach for six-legged robots is studied and experimented with a six-parallel-legged robot. Firstly, we propose a method whereby a vision sensor and a force/torque (F/T) sensor can be used to explore the relative location between the hole and peg. According to the visual information, the robot can approach the hole. Next, based on the force feedback, the robot plans the trajectory in real time to mate the peg and hole. Then, during the insertion, admittance control is implemented to guarantee the smooth insertion. In addition, during the whole assembly process, the peg is held by the gripper and attached to the robot body. Connected to the body, the peg has sufficient workspace and six DOF to perform the assembly task. Finally, experiments were conducted to prove the suitability of the approach.

A Survey on Mechanical Solutions for Hybrid Mobile Robots

This paper presents a survey on mobile robots as systems that can move in different environments with walking, flying and swimming up to solutions that combine those capabilities. The peculiarities of these mobile robots are analyzed with significant examples as references and a specific case study is presented as from the direct experiences of the authors for the robotic platform HeritageBot, in applications within the frame of Cultural Heritage. The hybrid design of mobile robots is explained as integration of different technologies to achieve robotic systems with full mobility.

Semi-Passive Walk and Active Walk by One Bipedal Robot: Mechanism, Control and Parameter Identification

We developed a bipedal robot equipped with brake and clutch mechanisms to change the number of active and passive joints, thereby enabling various types of movements including normal active walking using 12-dof joints, under-actuated walking using brake, and passive-based walking using clutch and passive joints. In this paper, we describe three technologies to achieve the proposed system and show experimental results on active and semi-passive walking. The first technology comprises a small and high-strength clutch mechanism to sustain the massive weight of life-sized robots using actuators for joint and dog clutch control. The second technology comprises a walking controller using a simulation-based optimization technique to consider passive joint dynamics instead of depending on the inverse kinematics problem, thereby enabling the control of the under-actuated leg. The last technology is model parameter identification to achieve unstable passive-based walking in real-world considering the body as well as environmental parameters such as ground slope. To the best of our knowledge, the proposed robot is the first to achieve both active and passive-based walking using a bipedal body. This enables the implementation of the passive-walking technology to active-joint robots and expands the application possibility of passive joint for bipedal robots.

Mechatronic Design of ARC Humanoid Robot Open Platform: First Fully 3D Printed Kid-Sized Robot

Although there are several popular and capable humanoid robot designs available in the kid-size range, they lack some important characteristics: affordability, being user-friendly, using a wide-angle camera, sufficient computational resources for advanced AI algorithms, and mechanical robustness and stability are the most important ones. Recent advances in 3D printer technology enables researchers to move from model to physical implementation relatively easy. Therefore, we introduce a novel fully 3D printed open platform humanoid robot design named ARC. In this paper, we discuss the mechanical structure and software architecture. We show the capabilities of the ARC design in a series of experimental evaluations.

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

Hands in the Real World

Robots face a rapidly expanding range of potential applications beyond controlled environments, from remote exploration and search-and-rescue to household assistance and agriculture. The focus of physical interaction is typically delegated to end-effectors-fixtures, grippers or hands-as these machines perform manual tasks. Yet, effective deployment of versatile robot hands in the real world is still limited to few examples, despite decades of dedicated research. In this paper we review hands that found application in the field, aiming to discuss open challenges with more articulated designs, discussing novel trends and perspectives. We hope to encourage swift development of capable robotic hands for long-term use in varied real world settings. The first part of the paper centers around progress in artificial hand design, identifying key functions for a variety of environments. The final part focuses on the overall trends in hand mechanics, sensors and control, and how performance and resiliency are qualified for real world deployment.

Design and Development of the Cassino Biped Locomotor

In this work, the Cassino Biped Locomotor, a biped walking robot, is presented as the leg design by using reduced parallel mechanisms. The proposed biped locomotor consists of two identical tripod leg mechanisms with a three degree-of-freedom parallel manipulator architecture. Kinematics analysis is carried out in terms of the forward and inverse kinematics of one leg mechanism and inverse kinematics of the biped locomotor. The walking operation is discussed in detail with gait planning and trajectories of feet and waist. A CAD model is elaborated in solidworks® environment and the corresponding prototype is fabricated with low-cost user-oriented features by using commercial components and structural parts that are manufactured by using 3D printing. An experimental layout and corresponding test modes are illustrated for characterizing the walking operation performance. Experimental results are analyzed for an operation performance evaluation and architecture design characterization of the Cassino Biped Locomotor.

Placing and scheduling many depth sensors for wide coverage and efficient mapping in versatile legged robots

This article tackles the problem of designing 3D perception systems for robots with high visual requirements, such as versatile legged robots capable of different locomotion styles. In order to guarantee high visual coverage in varied conditions (e.g., biped walking, quadruped walking, ladder climbing), such robots need to be equipped with a large number of sensors, while at the same time managing the computational requirements that arise from such a system. We tackle this problem at both levels: sensor placement (how many sensors to install on the robot and where) and run-time acquisition scheduling under computational constraints (not all sensors can be acquired and processed at the same time). Our first contribution is a methodology for designing perception systems with a large number of depth sensors scattered throughout the links of a robot, using multi-objective optimization for optimal trade-offs between visual coverage and the number of sensors. We estimate the Pareto front of these objectives through evolutionary optimization, and implement a solution on a real legged robot. Our formulation includes constraints on task-specific coverage and design symmetry, which lead to reliable coverage and fast convergence of the optimization problem. Our second contribution is an algorithm for lowering the computational burden of mapping with such a high number of sensors, formulated as an information-maximization problem with several sampling techniques for speed. Our final system uses 20 depth sensors scattered throughout the robot, which can either be acquired simultaneously or optimally scheduled for low CPU usage while maximizing mapping quality. We show that, when compared with state-of-the-art robotic platforms, our system has higher coverage across a higher number of tasks, thus being suitable for challenging environments and versatile robots. We also demonstrate that our scheduling algorithm allows higher mapping performance to be obtained than with naïve and state-of-the-art methods by leveraging on measures of information gain and self-occlusion at low computational costs.

Human-Inspired Online Path Planning and Biped Walking Realization in Unknown Environment

The focus of research in biped locomotion has moved toward real-life scenario applications, like walking on uneven terrain, passing through doors, climbing stairs and ladders. As a result, we are witnessing significant advances in the locomotion of biped robots, enabling them to move in hazardous environments while simultaneously accomplishing complex manipulation tasks. Yet, considering walking in an unknown environment, the efficiency of humanoid robots is still far from being comparable with the human. Currently, bipeds are very sensitive to external changes and they have severe constraints for adaptation of walk to conditions from such a complex environment. Promising approaches for efficient generation and realization of walking in a complex environment are based on biological solutions that have been developed for many years of evolution. This work presents one such human-inspired methodology for path planning and realization of biped walk appropriate for motion in a complex unfamiliar environment. Path planning results in calculating clothoid curves that represent well the human-like walking path. The robot walk is realized by the composition of parametric motion primitives. Such an approach enables on-line modification of planned path and walk parameters at any moment, instantly. To establish the relationship between high-level path planner and the low-level joint motion realization, we had to find a way to extract the parameters of the clothoid paths that can be linked with the parameters of the walk and consequently to motion primitive parameters. This enabled the robot to adopt its walking for avoiding the obstacles and for a smooth transition between different paths. In this paper we provide a complete framework that integrates the following components: (i) bio-inspired online path planning, (ii) path-dependent automatic calculation of high-level gait parameters (step length, walking speed, direction, and the height of the foot sole), and (iii) automatic calculation of low-level joint movements and corresponding control terms (driving motor voltage) through the adaptation of motion primitives which realize walking pattern and preserves the dynamic balance of the robot.

An Overview on Principles for Energy Efficient Robot Locomotion

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.

Benchmarking the HRP-2 Humanoid Robot During Locomotion

In this paper we report results on benchmarking a HRP-2 humanoid robot. The humanoid robots of this serie are known to be very robust. They have been successfully used by several research groups for the design of new motion generation algorithms. As such it is a reference in the category of electrically driven humanoid robot. As new humanoid robots are continuously built it is interesting to compare the performances of these new prototypes to those of HRP-2. This benchmarking study was realized through a campaign of measurements in an advanced equipped testing laboratory that provides a well adapted controlled environment. We have investigated the effect of temperatures variation on the robot walking capabilities. In order to benchmark various environmental conditions and algorithms we computed a set of performance indicators for bipedal locomotion. The scope of the algorithms for motion generation evaluated here ranges from analytical solution to numerical optimization approach, enabling real-time walking or multi-contacts motions.

Design and Modeling of a Lightweight and Low Power Consumption Full-Scale Biped Robot

This paper introduces the design methodology, the modeling and the power consumption tests for a newly developed biped robot equipped with 12 DOFs. The robot is 1.1 meters tall (lower limbs) which makes it comparable in dimension with other state-of-the-art full-scale humanoids. By using a combination of 3D printing techniques and lightweight materials, the system weighs only 10.8[Formula: see text]kg (without batteries) while retaining high links strength and rigidity. Without compromising the workspace dimension, the robot presents a very low weight-to-height ratio (9.8[Formula: see text]kg/m) that translates into a safer operation and reduced energy consumption. To perform elementary locomotion primitives, e.g., changing the support from one foot to the other or lifting its body, the robot prototype consumes only 65 watts. Simulation results demonstrate the suitability of the robot’s kinematics to perform walking motion and predict an average power consumption of 200 watts. The direct kinematics of the robot is presented together with its inverse dynamics based on a Chaotic Recurrent Neural Network (CRNN). The adaptive model is identified using a recursive least squares algorithm that allows the CRNN to predict the torques at different step lengths with a MSE of 0.0057 on normalized data.

Center-of-Mass-Based Grasp Pose Adaptation Using 3D Range and Force/Torque Sensing

Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose adaptation method, for picking up objects using a combination of exteroceptive 3D perception and proprioceptive force/torque sensor feedback. The method works in two iterative stages to provide reliable and wrist torque efficient grasps. Initially, a geometric object CoM is estimated from the input range data. In the first stage, a set of hand-size handle grasps are localized on the object and the closest to its CoM is selected for grasping. In the second stage, the object is lifted using a single arm, while the force and torque readings from the sensor on the wrist are monitored. Based on these readings, a displacement to the new CoM estimation is calculated. The object is released and the process is repeated until the wrist torque effort is minimized. The advantage of our method is the blending of both exteroceptive (3D range) and proprioceptive (force/torque) sensing for finding the grasp location that minimizes the wrist effort, potentially improving the reliability of the grasping and the subsequent manipulation task. We experimentally validate the proposed method by executing a number of tests on a set of objects that include handles, using the humanoid robot WALK-MAN.