International Journal of Humanoid Robotics

In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.

This study aims to investigate the perceived enjoyment and service quality of service robots with a user acceptance model to predict user acceptance of service robots in global markets. Service robots are currently providing various services in various fields. A total of 1014 users worldwide participated in an online survey to analyze our proposed research model. By using the method of structural equation modeling with maximum likelihood, this study finds that service quality and perceived enjoyment by users significantly affect the factors of usefulness and ease of use in adopting service robots for various tasks. Also, this study confirms the value of the traditional technology acceptance model. In other words, the present study suggests a new framework for understanding the perceptions of users about the emerging technology of service robots in various fields worldwide.

This article describes the hardware design of the iCub humanoid robot. The iCub is an open-source humanoid robotic platform designed explicitly to support research in embodied cognition. This paper covers the mechanical and electronic design of the first release of the robot. A series upgrades developed for the second version of the robot (iCub2), which are aimed at the improvement of the mechanical and sensing performance, are also described.

Humanoid robots that share the same space with humans need to be socially acceptable and effective as they interact with people. In this paper we focus our attention on the definition of a behavior-based robotic architecture that (1) allows the robot to navigate safely in a cluttered and dynamically changing domestic environment and (2) encodes embodied non-verbal interactions: the robot respects the users personal space (PS) by choosing the appropriate distance and direction of approach. The model of the PS is derived from human–robot interaction tests, and it is described in a convenient mathematical form. The robot's target location is dynamically inferred through the solution of a Bayesian filtering problem. The validation of the overall behavioral architecture shows that the robot is able to exhibit appropriate proxemic behavior.

Based on the screw theory and Lie group notations, this paper presents a modeling method for a kind of wheeled humanoid robot whose upper human-like body is mounted on the top of a mobile platform with three wheels. By combining the reciprocal product of the twist and wrench with Jourdain variation principle, a general formulation method is proposed to model the whole system's dynamics that represents directly the relationship between the input and the resultant external and inertial wrench. Both the system kinematics and dynamics are derived carefully. The simulations are made to verify the proposed modeling methodology and the simulation results are also compared with the results obtained from the multi-body dynamics software.

It is a quite challenging problem for robots to track the target in complex environment due to appearance changes of the target and background, large variation of motion, partial and full occlusion, motion of the camera and so on. However, humans are capable to cope with these difficulties by using their cognitive capability, mainly including the visual attention and learning mechanisms. This paper therefore presents a single-object tracking method for robots based on the object-based attention mechanism. This tracking method consists of four modules: pre-attentive segmentation, top-down attentional selection, post-attentive processing and online learning of the target model. The pre-attentive segmentation module first divides the scene into uniform proto-objects. Then the top-down attention module selects one proto-object over the predicted region by using a discriminative feature of the target. The post-attentive processing module then validates the attended proto-object. If it is confirmed to be the target, it is used to obtain the complete target region. Otherwise, the recovery mechanism is automatically triggered to globally search for the target. Given the complete target region, the online learning algorithm autonomously updates the target model, which consists of appearance and saliency components. The saliency component is used to automatically select a discriminative feature for top-down attention, while the appearance component is used for bias estimation in the top-down attention module and validation in the post-attentive processing module. Experiments have shown that this proposed method outperforms other algorithms without using attention for tracking a single target in cluttered and dynamically changing environment.

Natural human walking possesses three characteristics: (i) gait repeatability, (ii) postural balance, and (iii) highly regulated centroidal angular momentum (CAM). In this paper, a systematic gait-planning framework is presented for the gait planning of a five-link bipedal robot, negotiating those three characteristics. The framework employs a set of task-space variables and a set of gait parameters. Five kinematic and dynamic objective functions are selected, corresponding to the biped's five degrees of freedom (DOFs), incorporating three characteristics. Fusing the equations of five objective functions together, two ordinary differential equations called the framework equations are derived. Assigning desired values to the gait parameters, the framework equations are integrated across the gait cycle, rendering the motion profiles of the task-space variables. A set of simulation results shows that the framework presents gait that successfully negotiates three characteristics. A parametric analysis is then carried out to study the effect of changing the gait parameters on the joint angular displacements and velocities, the postural balance, and the regulation of CAM of the bipedal robot.

Most of the studies on biped walking on even or uneven terrain have assumed stiff ground. This paper proposes a dynamic contact model between foot and loose soil. The proposed contact model provides sinkage of the foot, slip of the sole and reactive force acting on the foot on loose soil. Sinkage of the foot and slip of the sole are calculated utilizing terramechanics model, which are important characteristics for biped robot to walk on loose soil. Reactive force acting on the foot on loose soil is calculated using spring-damper model between the foot and the deformed ground. By applying the proposed contact model to a usual dynamics simulator, dynamic sinkage and slip phenomenon during biped walking on loose soil are simulated. Additionally, in order to verify the simulation result, experiments were carried out using a humanoid robot.

This paper is aimed at describing a technique to compensate undesired yaw moment, which is inevitably induced about the support foot during single support phases while a bipedal robot is in motion. The main strategy in this method is to rotate the upper body in a way to exert a secondary moment that counteracts to the factors which create the undesired moment. In order to compute the yaw moment by considering all the factors, we utilized Eulerian ZMP Resolution, as it is capable of characterizing the robot's rotational inertia, a crucial component of its dynamics. In doing so, intrinsic angular momentum rate changes are smoothly included in yaw moment equations. Applying the proposed technique, we conducted several bipedal walking experiments using the actual bipedal robot CoMan. As the result, we obtained 61% decrease in undesired yaw moment and 82% regulation in yaw-axis deviation, which satisfactorily verify the efficiency of the proposed approach, in comparison to off-the-shelf techniques.

This paper presents a coupled and directly self-adaptive underactuated (CDSA) grasp mode, which has hybrid functions: coupled grasp and directly self-adaptive grasp. A novel multi-pulley-belt finger mechanism with springs is designed based on CDSA grasp mode. Compared with traditional coupled underactuated fingers or directly self-adaptive underactuated fingers, the grasp process of the new finger is more humanoid and its stability is better. Force analysis and size optimization rules of the finger are given. A multi-fingered robotic hand, CDSA hand, based on the CDSA finger is developed, whose control system adopts a digital signal processor (DSP) circuit module with a keyboard as its communication interface. The CDSA hand has five fingers, six DC motors, and 15 joint DOF, nine of which are CDSA joint DOF. The appearance and actions of CDSA hand imitate human hand, whose size is 1.5 times of human hand. CDSA hand weighs 1.6 kg and can grasp objects up to 0.5 kg. Simulation and experimental results show that CDSA hand is able to realize CDSA grasp mode effectively.

In this work we show how precomputed reachability information can be used to efficiently solve complex inverse kinematics (IK) problems such as bimanual grasping or re-grasping for humanoid robots. We present an integrated approach which generates collision-free IK solutions in cluttered environments while handling multiple potential grasping configurations for an object. Therefore, the spatial reachability of the robot's workspace is efficiently encoded by discretized data structures and sampling-based techniques are used to handle arbitrary kinematic chains. The algorithms are employed for single-handed and bimanual grasping tasks with fixed robot base position and methods are developed that allow to efficiently incorporate the search for suitable robot locations. The approach is evaluated in different scenarios with the humanoid robot ARMAR-III.