Search results

The purpose of this study is to present an optimization-based gait planning method for biped robots according to the conditions of terrain, which takes fully the relationship between walking stability margin and energy efficiency into account.

First, the authors newly designed a practical gait motion synthesis algorithm by using the optimal allowable zero moment point (ZMP) variation region (OAZR), which can generate different gait motions corresponding to different terrains based on the modifiability of ZMP in lateral (y-axis) direction. Second, an effective gait parameter optimization algorithm is performed to find the optimal set of key gait parameters (step length, duration time of gait cycle, average height of center of mass (CoM), amplitude of the vertical CoM motion and double support ratio), which maximizes either the walking stability margin or the energy efficiency with certain walking stability margin under practical constraints (mechanical constraints of all joint motors, geometric constraints, friction force limit and yawing moment limit) according to the conditions of terrain. Third, the necessary controllers for biped robots have been introduced briefly.

The experiment data and results are described and analyzed, showing that the proposed method was verified through simulations and implemented on a DRC-XT biped robot.

The main contribution is that the OAZR has been defined based on AZR, which could be used to plan and generate the various feasible gait motions to help a biped robot to adapt effectively to various terrains.

The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique.

Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution).

The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude.

This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

Going upstairs is a common humanoid robot task. In this paper, a genetic algorithm (GA) gait synthesis method for going upstairs and a radial basis function neural network (RBFNN) implementation, are considered. The gait synthesis is analyzed based on the minimum consumed energy and minimum torque change. The proposed method can easily be applied to generate the angle trajectories for going downstairs, overcoming obstacles, etc. In our work, the stability is verified through the ZMP concept. For the real time implementation, a RBFNN which is taught based on the GA results, is considered. The RBFNN generates the optimal gait in a very short time, where the input variables are the step length, step height and step time. Simulations are realized based on the parameters of the “Bonten‐Maru I” humanoid robot.

This paper contributes to the problem of humanoid robot gait generation in unknown environments. The intention of the proposed method is to create an autonomous humanoid robot, able to take decisions and generate the appropriate optimal gait based on the information received by the eye system. Up to now, we have created two modules: walking and going upstairs. In order to create an autonomous humanoid robot, we plan to consider other tasks like going downstairs, creeping, obstacle overcoming, etc. In this paper, we present the simulation and experimental results for real time humanoid robot gait generation realized with the “Bonten‐Maru I” humanoid robot. The results showed that the Neural Network modules generate in a very short time a stable humanoid robot motion.

The purpose of this paper is to introduce a quadruped robot strategy to avoid tipping down because of side impact disturbance and a control algorithm that guarantees the strategy can be controlled stably even in the presence of disturbances or model uncertainties.

A quadruped robot was developed. Trot gait is applied so the quadruped can be modelled as a compass biped model. The algorithm to find a correct stepping position after an impact was developed. A particle swarm optimization-based structure-specified mixed sensitivity (H₂/H_∞) robust is applied to reach the stepping position.

By measuring the angle and speed of the side tipping after an impact disturbance, a point location for the robot to step or the foothold recovery point (FRP) was successfully generated. The proposed particle swarm optimization-based structure-specified mixed sensitivity H₂/H_∞ robust control also successfully brought the legs to the desired point.

A traditional H_∞ controller synthesis usually results in a very high order of controller. This makes implementation on an embedded controller very difficult. The proposed controller is just a second-order controller but it can handle the uncertainties and disturbances that arise and guarantee that FRP can be reached.

The first contribution is the proposed low-order robust H₂/H_∞ controller so it is easy to be programmed on a small embedded system. The second is FRP, a stepping point for a quadruped robot after receiving side impact disturbance so the robot will not fall.

The purpose of this paper is to present a coordinated control strategy for stable walking of biped robot with heterogeneous legs (BRHL), which consists of artificial leg (AL) and intelligent bionic leg (IBL).

The original concentrated control in common biped robot system is replaced by a master‐slave dual‐leg coordinated control. P‐type open/closed‐loop iterative learning control is used to realize the time‐varying gait tracking for IBL to AL.

The new control architecture can simplify gait planning scheme of BRHL system with complicated closed‐chain mechanism and mixed driving mode.

Designing and constructing a suitable magneto‐rheological damper can greatly improve the control performance of IBL.

Master‐slave coordination strategy is suitable for BRHL stable walking control.

The concepts and methods of dual‐leg coordination have not been explicitly proposed in single biped robot control research before. Master‐slave coordinated control strategy is suitable for complicated BRHL.

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.

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.

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.

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.

The paper aims to present a new mechanical scheme for a leg to be included in legged vehicles that simplifies the control actuations along the stride.

The scheme includes three four‐bar links grouped in two mechanisms. The first one decouples the vertical and horizontal foot movements. The second one produces a constant horizontal foot velocity when the corresponding motor is given a constant speed. A hybrid robot with wheels at the end of the hind legs has been simulated and constructed to validate the leg performance.

The gait control requires only five commands for the electronic cards to control the leg. Decoupling vertical and horizontal movements allows a more adequate selection of actuators, a reduction of energy consumption, and higher load capacity and robot velocity. Additional mechanical benefits, such as improved robustness and lower inertia, are obtained. The hind legs can also be articulated, allowing the robot to overcome an obstacle and to climb up and down stairs.

A hybrid robot offers greater stability with respect to a legged robot. This way the lateral movement is not a concern, and therefore it has not been tested yet during the walking cycle.

This new scheme obtains a quasi‐Cartesian behaviour for the foot movement that drastically simplifies the control of the walking cycle. Although the decoupling between movements has already been obtained in previous configurations, these follow a pantograph structure and suffer from blocking problems when they are subject to lateral forces. These schemes were suitable for crab‐like gaits. The proposed leg moves according to a mammal‐like gait.

Walking on inclined ground is an important ability for humanoid robots. In general, conventional strategies for walking on slopes lack technical analysis in, first, the waist posture with respect to actual robot and, second, the landing impact, which weakens the walking stability. The purpose of this paper is to propose a generic method for walking pattern generation considering these issues with the aim of enabling humanoid robot to walk dynamically on a slope.

First, a virtual ground method (VGM) is proposed to give a continuous and intuitive zero-moment point (ZMP) on slopes. Then, the dynamic motion equations are derived based on 2D and 3D models, respectively, by using VGM. Furthermore, the waist posture with respect to the actual robot is analyzed. Finally, a reformative linear inverted pendulum (LIP) named the asymmetric linear inverted pendulum (ALIP) is proposed to achieve stable and dynamical walking in any direction on a slope with lower landing impact.

Simulations and experiments are carried out using the DRC-XT humanoid robot platform with the aim of verifying the validity and feasibility of these new methods. ALIP with consideration of waist posture is practical in extending the ability of walking on slopes for humanoid robots.

A generic method called ALIP for humanoid robots walking on slopes is proposed. ALIP is based on LIP and several changes, including model analysis, motion equations and ZMP functions, are discussed.

Lower‐limb exoskeletons and powered orthoses are external devices that assist patients with locomotive disorders to achieve correct limb movements. Current batteries cannot meet the long‐term power requirements for these devices, which operate for long periods of time. This issue has become a major challenge in the development of these portable robots. Conversely, legged locomotion in animals and humans is efficient; to emulate this behaviour, biomimetic actuation has been designed attempting to incorporate elements that resemble biological elements, such as tendons and muscles, in the mechanical systems. The purpose of this paper is to present a mechanism that resembles a human tendon to achieve and utilise the synergic actuation of the leg joints.

In this paper, we present a mechanism that resembles a human tendon to move the ankle joint and utilise the synergic actuation of hip and knee joints. Implementation of the proposed transmission system in the ATLAS active orthosis prototype allowed for a better ankle gait fit, which resulted in a more natural stride and, as expected, optimised energy consumption in the locomotion cycle and actuation energy requirements.

The fitted passive ankle motion provides toe‐off impulse, increases support force, and helps provide ground clearance.

A synergetic underactuated movement in the ankle joint, implemented by two cables in each leg, improves the functionality of the device without increasing the leg weight and while maintaining a reduced size. To achieve a correct and efficient motion in the ankle of an active orthosis, two steel cables were attached in the ATLAS orthosis. These cables act as a synergic biarticular linkage and transfer motion from the hip and knee joints. Synergic ankle motion provides impulse in the toe‐off, increases support force, and provides ground clearance. These goals are achieved with low energy expenditure because of synergical actuation, and high inertia is prevented in the more distal limb.