Motion Control Of Humanoid Robots Research Articles

Survey on model-based biped motion control for humanoid robots

ABSTRACT Studies of biped control including standing, walking, hopping and running on humanoid robots are reviewed in this survey paper. Model-based approaches standing upon reduced-order dynamics are focused. It begins with revisiting the centroidal dynamics and the zero-moment point, which leads to the linear inverted pendulum (LIP) model and some of its variations. Then, some representative standing and walking control techniques based on the LIP model are reviewed, where the concept of capturability is also discussed. It is followed by consideration of height variation of the center of mass. Enhancement to hopping and running motions, which include aerial phases, is also addressed. Ideas to deal with complex nature as a hybrid system underlying discontinuously varying mechanics mainly due to unilateral constraints on contact forces are concisely explained.

Deep reinforcement learning-based attitude motion control for humanoid robots with stability constraints

Purpose This paper aims to present an intelligent motion attitude control algorithm, which is used to solve the poor precision problems of motion-manipulation control and the problems of motion balance of humanoid robots. Aiming at the problems of a few physical training samples and low efficiency, this paper proposes an offline pre-training of the attitude controller using the identification model as a priori knowledge of online training in the real physical environment. Design/methodology/approach The deep reinforcement learning (DRL) of continuous motion and continuous state space is applied to motion attitude control of humanoid robots and the robot motion intelligent attitude controller is constructed. Combined with the stability analysis of the training process and control process, the stability constraints of the training process and control process are established and the correctness of the constraints is demonstrated in the experiment. Findings Comparing with the proportion integration differentiation (PID) controller, PID + MPC controller and MPC + DOB controller in the humanoid robots environment transition walking experiment, the standard deviation of the tracking error of robots’ upper body pitch attitude trajectory under the control of the intelligent attitude controller is reduced by 60.37 per cent, 44.17 per cent and 26.58 per cent. Originality/value Using an intelligent motion attitude control algorithm to deal with the strong coupling nonlinear problem in biped robots walking can simplify the control process. The offline pre-training of the attitude controller using the identification model as a priori knowledge of online training in the real physical environment makes up the problems of a few physical training samples and low efficiency. The result of using the theory described in this paper shows the performance of the motion-manipulation control precision and motion balance of humanoid robots and provides some inspiration for the application of using DRL in biped robots walking attitude control.

Hybrid motion control of humanoid robot for leader-follower cooperative tasks

This paper presents a framework for leader-follower type cooperative transportation of an object by multiple humanoid robots or a single robot and a human. The emphasis in this paper is on the hybrid control algorithm and motion generation of the follower robot, while the influence of the leader has been simulated as external force acting on the follower?s hands. The presented approach uses impedance controller to provide compliant behavior of robot arms and it is an extension of our previous work on dual-mode impedance controller for safe human-robot interaction. Synthesis of the follower?s legs and trunk motion is based on the reconfigurable adaptive motion primitives, which are defined as simple, parameterized motion building blocks that can be combined in a sequence or in parallel to generate complex motion. It has been already proven that motion generation, based on reconfigurable adaptive primitives enables the robot to modify gait parameters online, at any time instant, and to synthesize dynamically balanced walk. Motion of the follower is based on the reactive approach, where the gait parameters (walking velocity, direction and step length) depend on the intensity and the direction of the external force vector. Robot end-effectors are compliant in the horizontal plain, adapting to the physical guidance of the leader, while being stiff in the vertical direction in order to compensate the external force in negative Z direction. The proposed framework has been tested by numerical simulations involving a dynamic robot model.

Application of Camera Image Processing to Control of Humanoid Robot Motion

The wide potential applications of humanoid robots require that the robots can move in general environment, overcome various obstacles, detect predefined objects and control of its motion according to all these parameters. The goal of this paper is address the problem of implementation of computer vision to motion control of humanoid robot. We focus on using of computer vision and image processing techniques, based on which the robot can detect and recognize a predefined color object in a captured image. An algorithm to detection and localization of objects is described. The results obtained from image processing are used in an algorithm for controlling of the robot movement.

経験からの知識獲得に基づいたヒューマノイドの運動制御

This paper proposes a KDD (knowledge discovery and data mining) approach to motion control for humanoid robots. The aim of this approach is to discover the knowledge for generating stable motions in balance. In this paper, we propose a motion control system, which can generate a stable and anti-tumble motion by the concept learning and the searching in the motion space: extracting some generalized motion guideposts by decision tree learner and motion generation with tracking the guidepost by hill-climbing search. The motion control system has three parts: training, learning, and generating part. Former two parts are to acquire the balancing property of itself body and movement by decision tree learning for numbers of executed training motions, and latter one part provides the search technique for the motion control based on the acquired knowledge concerning balance and stability in the motion. In this paper, some performance results by humanoid robot HOAP-1 are reported: stable and anti-tumble motions to stand up from a chair. This paper also reports some performance for the change in the environments: standing up from a chair on slope and different in height.

時間二重外乱吸収法に基づくヒューマノイドロボットの全身協調運動制御

A pattern-based approach towards the motion control of humanoid robots. which reduces its difficulty due to the strong nonlinearity, has to be assisted by an online stabilization to absorb disturbances in the real world. This paper proposes a stabilization method for humanoid robots, based on an idea of Dual Term Absorption of Disturbance, which simultaneously absorbs the error in both force pattern and kinematic pattern. These conflicting schemes are solved in accordance with the difference of time span, namely, force condition should be considered in short term, while kinematic condition tolerates to be considered rather in long term. The fact that such force-kinematic condition has to be taken into account arises from the absence of fixed points in the inertia frame. The advantage of proposed is that it allows to choose any combination of joints as modified properties, so that it is applicable for various types of robots and motions.