Development of an indirect-type teleoperation interface for biped humanoid robots

In this work, we present a novel method for ankle/foot compliance for biped humanoid robots walking over uneven terrain. Based on distributed plantar proximity sensing, we developed the Preemptive Foot Compliance (PFC) control that generates a Preemptive Ground Reaction Wrench that modifies the foot orientation to maximize the largest contact area to reduce the impact force produced at landing. PFC can be easily included in any walking controller for flat ground and become capable of walking on uneven terrains. The PFC was tested on two full-size humanoid robots running two different walking controllers, originally designed only for flat-ground walking. Both robots increase their capability to walk over uneven terrain and the walking impacts in flat grown were reduced by approximately 80%.

International audience

Gait pattern performance, for its crucial significance in humanoid robot stabilization, has attracted the attention of researchers worldwide. Although simplified models highlight major features, bipedal walking has bewildered the researchers. Therefore, for a precise understanding of the bipedal model, a state-of-the-art, simplified model has been proposed in this paper which comprises a 3D-multilinked dual spring-loaded inverted pendulum (3D-MDSLIP) while acknowledging the vertical fluctuations of the center of mass (CoM). In addition, the model considers upper body movement and its effects on the stabilization of the humanoid robot. The mathematical modeling of a humanoid walking over the obstacle and slope is demonstrated to precisely understand the problem. The tuning process of the parameters and postures in a humanoid robot is complex and time-consuming. For proper walking of a robot over uneven terrains and slopes, tuning of the PID controller is achieved using converged teaching-learning based optimization (CTLBO) technique for a central pattern generator (CPG) gait, as introduced in the paper. The optimal gait angles are applied to the experimental and simulated NAO to successfully navigate the provided terrain. Thus, the experimental and simulation results jointly show that the proposed CPG-CTLBO gait learning technique is feasible for finding an optimal gait pattern for the humanoid robot within a deviation of 5%. The energy efficiency of the proposed controller is compared with the default controller of NAO based on the average electronic current in sagittal and lateral movement. Further, it is examined for the energy consumption for several slopes, and the results obtained are acceptable, showing the controller is efficient. Additionally, it has been compared with an existing technique for walking a humanoid robot on uneven terrains. The graph obtained using the proposed technique demonstrates the superiority of the proposed technique.

In this paper, we present a method to perform tasks identification on a humanoid robot. The observed motion is compared to a set of candidate controllers that the robot might be executing. The more relevant candidate controllers are selected, and can be used as a description of the motion, or as a basis to replicate a similar motion on another robot. The analysis of the movements is based on the task-function approach and is applied in the context of humanoid robot motions. A pool of tasks is defined in order to cover the range of possible motion of the robot and the demonstration movement is projected in each candidate task of that pool. The reduced trajectory is then compared with the characteristic task trajectory using a numerical optimization process. The movement is then projected into the null space of the best task candidate. As a consequence, the motion due to the task candidate is subtracted from the original movement. This process is iterated until the result of a projection becomes null. The approach is applied to recognize the tasks performed by a HRP-2 robot in simulation, in order to disambiguate very similar-looking motions. Preliminary directions are given for possible application for human motion recognition.

The Large-scale uneven ground is mostly unsmooth and in the irregular state in which the irregularity is usually unknown (such as footway, carriageway, unexplored desert, mountainous area, surface of the Mars). In order to walk steadily and rapidly on such ground, a chief problem to overcome is that the humanoid robot's foot is not matching with the unknown model of the ground. Therefore, the humanoid robot's foot should have the flexibility mimic the human's foot, and can adapt to and steadily interacted with any unsmooth ground. Also interferences and disjoints between soles and irregular unsmooth ground should be avoided. Aiming at solving the key problem for humanoid robot to steadily and rapidly walk on the large-scale and three- dimension uneven ground, we've set up a new multidegree- of-freedom flexible foot mechanism which can make the humanoid robot's walking gesture and gait automatically adapt to the terrain, and then carry out the research of the globally stable control of the gait. The flexibility can obviously improve the humanoid robot's walking stability and speediness on the large-scale uneven ground.

In the coming decades, humanoid robots will play a rising role in society. The present article discusses their walking control and obstacle avoidance on uneven terrain using enhanced spring-loaded inverted pendulum model (ESLIP). The SLIP model is enhanced by tuning it with an adaptive particle swarm optimization (APSO) approach. It helps the humanoid robot to reach closer to the obstacles in order to optimize the turning angle to optimize the path length. The desired trajectory, along with the sensory data, is provided to the SLIP model, which creates compatible COM (center of mass) dynamics for stable walking. This output is fed to APSO as input, which adjusts the placement of the foot during interaction with uneven surfaces and obstacles. It provides an optimum turning angle for shunning the obstacles and ensures the shortest path length. Simulation has been carried out in a 3D simulator based on the proposed controller and SLIP controller in uneven terrain.

The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.

Teleoperation systems for humanoid robots that replace human tasks in emergency situations have been widely studied. Humanoid robots have the capability of utilizing the equipment designed for human manipulation. When the humanoid robot works on behalf of human beings, rapid deployment can be achieved when a disaster occurs. However, since the humanoid robot and the human have different physical abilities, it is difficult to control the robot by mapping the human motion directly to the robot. Recently, the interfaces that map the human motion, taken by the vision sensors, to the humanoid robot have become popular. Though the vision-based teleoperation system can be rapidly constructed anywhere, the interfaces require some sort of feedback to smoothly control the robot. The tactile and force feedback system has limitations on human motion because of mechanical structures. The teleoperation system also has the potential issue of time delay. In this paper, we propose an interface for teleoperation of the humanoid robot with vision sensors. The inner states of the robot and the recognized human motions are visualized for the feedback to the operator. We also introduce the task model to avoid the time delay. In the visual feedback, the task states are visualized to assist the smooth operations.

Walking locomotion has been proved as an adequate technique for autonomous motion on an uneven terrain. However, natural scenarios can be extremely complex and human intervention is regularly required. In such a case, a dialogue system known as human–machine interface (HMI) is essential for interrelation between human and machine. The objective of this paper is to present a human–machine interface developed for a four-legged robot. This HMI is based on a multi-modal interface, which integrates some ideas about collaborative control. It was developed as an operator's interface for guiding a walking robot, but it can also be used as a tool for analysing and debugging new gait algorithms. The interface includes some methods for defining robot trajectory and graphic representations for gaining information on both the robot and its environment. This work is tailored to the SILO4 walking robot; therefore, the SILO4 mechanism and control system are first introduced to lay out the possibilities of this new walking robot. Then, the main concepts, on which the HMI design is based, are introduced prior to the description of the different modules and functionality of the human–machine interface. Finally, some simulations and experiments with the SILO4 walking robot have been conducted to validate the performance of the HMI, presented in this article.

This work presents human-inspired control strategies required for achieving three motion primitives in walking-flat-ground, uneven terrain and up-slope-in an underactuated physical bipedal robot: AMBER. Formal models and controllers which provably guarantee the stability of walking are developed and verified in the simulation. Computationally tractable conditions are given that allow for the experimental implementation of these formal methods through the closed form approximation of constraints that restrict maximum torque, maximum velocity and ensure proper foot clearance. Considering the special property of the motors used in the robot, i.e., low leakage inductance and high angular speed, we approximate the motor model and translate the formal controllers satisfying these constraints into an efficient voltage-based controller that can be directly implemented on AMBER. The end result is robotic walking on AMBER for the three motion primitives that shows good agreement with the formal results from which it was derived.

As a part of motion control categories as discussed in Chap. 1, force and impedance control play a main role in legged/walking robot walking on unstructured or uneven terrain. With active suspension configuration (legs), the strong role of legged/walking robot design is capable of passing through any uneven terrain as long as the obstacles are not achieved its maximum or minimum overall body height, if compare to the wheel-type robot. Therefore, force or impedance control is needed to make a dynamic response on each leg in order to identify the different level of the terrain or any sudden changes on the terrain. Moreover, this control is very crucial on the hidden area that could not be identified by a vision system via pre-scanning and localization.KeywordsSwing PhaseImpedance ControlRough TerrainSoft GroundLegged RobotThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Control of humanoid robot behavior with the mind begins a new era of robotics research. One of the critical issues in this research is how to acquire the brain signals with high quality which are correlated to humanoid robot behavior. In order to improve subjects’ concentration on their mental activities during tests, we develop a stimuli module in the Cerebot system, consisting of a Cerebus neural data acquisition system and a Kumotek robot with 20 degrees of freedom or a NAO robot with 25 degrees of freedom. We present the experimental procedures for acquiring brain signals of imagining humanoid robot walking behavior by using movies of robot walking or real robot walking activities. We record two groups of brain signals correlated to mental activities of six robot walking behavior. Finally, we present a demonstration of controlling the humanoid robot walking behavior using the phase coding mechanisms of the Delta rhythms.KeywordsMind controlHumanoid robotRobot walking behaviorBrainwavesPhase coding

This paper presents an innovative algorithm for improvement of the walking robot dynamic stability, using the Desert Smarandache Theory (DSmT) and the neutrosophic logic set. Starting from the hybrid force-position control, by applying the Robot Neutrosophic Control (RNC) method we extend the fuzzy control for walking robot stability motion on uneven terrain and in unstructured environments. Results analysis through virtual experimentation for motion control of a mobile walking robot proves that the neutrosophic logic leads to performance improvement of real time control through robustness, adaptation to robot's environment, efficient control and fast and clear identification of robot states.

This paper proposed a simple but effective gait algorithm for hexapod robot to navigate in an uneven and rough terrain. The robot has six legs with three joints in each leg and actuated with 18 servo motors. The tactile sensor, which is simply a normally open push button, is placed in each end of leg to sense if the leg touch the ground. The main purpose of this research is to make the robot able to walk in an uneven terrain with the simple gait algorithm. The inverse kinematics method is used to calculate every angle of the joint from the given desired position of six legs. The implementation result in the end of this paper shows this algorithm succeed to make the robot navigate in rough an unstructured terrain.

Synthetic grass surface is a new rule in international robot soccer competition (RoboCup). The main issue in the development of the RoboCup competition today is about how to make a humanoid robot walk above the field of synthetic grass. Because of that, the humanoid robot needs a system that can be implemented into the walking algorithm. This paper describes how to maintain the stability of humanoid robot called EROS by using walking trajectory algorithm without a control system. The establishment of the walking trajectory system is combined with a process of landing optimization using deceleration and heel-strikes gait optimization. This system has been implemented into a humanoid robot with 52 cm of height and walking on synthetic grass with different speeds. By adding optimization, the robot walks more stable from 32% to 80% of stability. In the next research, the control system will be added to improve the stability.