International Journal of Humanoid Robotics

Although in recent years, there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy object. Such a functionality is especially useful when transporting objects of different shapes and weights without having to modify the robot hardware. In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply could not be moved by a single robot. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajectories to the design of the control to execute those trajectories. We present experiments made on real NAO robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without modifying the robot main hardware, and therefore that our scheme enhances the humanoid robots capacities in real-life situations.

Our contributions are: (1) a low-dimension multi-robot motion planning algorithm that finds an obstacle-free trajectory, by using the constructed map of the environment as an input, (2) a framework that produces continuous and consistent odometry data, by fusing the visual and the robot odometry information, (3) a synchronization system that uses the projection of the robots based on their hands positions coupled with the visual feedback error computed from a frontal camera, (4) an efficient real-time whole-body control scheme that controls the motions of the closed-loop robot–object–robot system.

The time of arrival trilateration method is one of the representative algorithms for the location detection technology, which estimates the location of mobile station (MS) at a unique intersection point of three circles with radiuses corresponding to distances between MS and base stations (BSs) and centers corresponding to coordinates of BSs. However, there may be serious estimation errors, when they do not meet at a point because the estimated radiuses of them are increased. The solutions for reducing the estimation position error in the main case of meeting three circles with the extended radius have been recently provided as the shortest distance algorithm and the line intersection algorithm. In general, they have good performance for the location estimation, but they may have serious errors in some cases. In this paper, we propose the efficient location estimation algorithm for the specific case of two large circles and one relatively small circle, which is located in the area of two large circles. In this case, there are six intersections in total based on the three extended circles and a small circle has four intersections with two large circles. The proposed approach compares four distances based on four neighboring intersections and selects the shortest one. Finally, it determines the averaged coordinate of two intersections corresponding to the shortest distance, as the location of MS. The location-estimating performance of the proposed algorithm is illustrated by the computer simulation example.

Joint attention is a communicative activity that allows social partners to share perceptual experiences by jointly attending to an environmental object. Unlike the common approach towards joint attention, which is based on the developmental view in robotics, here it is conceptualized with a psychophysical paradigm known as cueing. The triadic interaction of joint attention is formalized as the conditional probability of an attentional response for a given target candidate derived from object features and a cue derived from a human partner's indication. A robotic system to which the joint attention model is applied conducted a series of tasks to demonstrate the properties of the computational model. The robotic system successfully performed the tasks, which could not be specified by the information derived from a target object alone; furthermore, the system demonstrated how perceptual and selection ambiguity is resolved through joint attentive interaction and made to converge into a common perceptual state. The results imply that a perceptual common ground is constructed on the triadic relationship between user, robot, and objects through joint attentive interaction.

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

Using active vision to perceive surroundings instead of just passively receiving information, humans develop the ability to explore unknown environments. Humanoid robot active vision research has already half a century history. It covers comprehensive research areas and plenty of studies have been done. Nowadays, the new trend is to use a stereo setup or a Kinect with neck movements to realize active vision. However, human perception is a combination of eye and neck movements. This paper presents an advanced active vision system that works in a similar way as human vision. The main contributions are: a design of a set of controllers that mimic eye and neck movements, including saccade eye movements, pursuit eye movements, vestibulo-ocular reflex eye movements and vergence eye movements; an adaptive selection mechanism based on properties of objects to automatically choose an optimal tracking algorithm; a novel Multimodal Visual Odometry Perception method that combines stereopsis and convergence to enable robots to perform both precise action in action space and scene exploration in personal space. Experimental results prove the effectiveness and robustness of our system. Besides, the system works in real-time constraints with low-cost cameras and motors, providing an affordable solution for industrial applications.

Balancing motions are usually designed using simplified models of the Center of Mass (CoM) and feedback control without accounting for energy efficiency. In order to tackle this shortcoming, we introduce a Motion Primitive switching methodology where samples of optimal motions (Motion Primitives) are chosen online based on a Euclidean distance metric. The chosen sample is used to provide reference trajectories, torques and ground reaction forces to be tracked. In order to satisfy all of the modeling assumptions while tracking the reference values, a Quadratic Program (QP) is solved online where the dynamics of the robot, friction, Center of Pressure and torque bounds are treated as constraints. Convergence to the desired trajectories is dictated by a Control Lyapunov Function constraint which is introduced in the QP. The methodology is evaluated on a four-link simulated robot where we show that switching between Motion Primitives provides energy efficient balancing motions for different disturbance situations. At the same time the methodology provides more efficient motions for different disturbance forces when compared to a nonswitching approach, where a Motion Primitive is chosen only once at the beginning.

When executing tasks, robots are required to demonstrate compliance to unexpected external disturbances or human–robot interactions, and return to the demanded posture when the disturbances or contacts are removed. Traditional Virtual Model Control (VMC) requires precise gravitational compensation to accurately control the posture of a robot. Hence, load variations or other uncertain unmodeled factors in the robot will result in offsets to its balance posture, which makes the robot deviate from the demanded posture when it is in a static state. To reject this offset without sacrificing the compliance of the robot, an adaptive controller is proposed in this paper to implement adaptive compliance on the robot, which makes the robot robust to the variations in gravitational loads in the double leg support phase. The adaptive controller is a combination of the VMC controller and an online gravitational loads estimator, in which the estimator is derived in the double leg support phase to estimate the values of these parameters and obtains an online updating law based on a Kalman optimal estimator. Then, a Lyapunov function is designed to modify and combine the controller and the online gravitational loads estimator. The experiments are conducted on a 4 DoF bipedal robot in the sagittal plane to validate the effectiveness of the controller and show that, by estimating the gravitational loads of the robot, the effects of load variations on balance posture are rejected without sacrificing compliance.

Precise task-space tracking with manipulator-type systems requires an accurate kinematic model. In contrast to traditional manipulators, sometimes it is difficult to obtain an accurate kinematic model of humanoid robots due to complex structure and link flexibility. Also, prolonged use of the robot will lead to some parts wearing out or being replaced with a slightly different alignment, thus throwing off the initial calibration. Therefore, there is a need to develop a control algorithm that can compensate for the modeling errors and quickly retune itself, if needed, taking into account the controller bandwidth limitations and high dimensionality of the system. In this paper, we develop an iterative learning control algorithm that can work with existing inverse kinematics solvers to refine the joint-level control commands to enable precise tracking in the task space. We demonstrate the efficacy of the algorithm on a theme-park-type humanoid doing a drawing task, serving drink in a glass, and serving a drink placed on a tray without spilling. The iterative learning control algorithm is able to reduce the tracking error by at least two orders of magnitude in less than 20 trials.