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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.

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.

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.

Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel).

The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot.

The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller.

Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

This paper aims to propose a pulsing type joint servo driver-based obstacle surmounting method for a humanoid robot according to the whole-body dynamics model, which fully takes into account the relationship between the whole-body stability margin and instantaneous torque.

First, the authors designed a new practical instantaneous large torque strategy for a pulsing type joint servo driver by modeling the whole-body dynamics of the humanoid robot. The work also considered joint angle planning based on the dynamic model for crossing obstacles. Second, in the simulation and experimentation, the instantaneous torque of the driver is used to realize successful crossing of obstacles by the humanoid robot. This verifies the correctness of the whole-body dynamics model and the feasibility of the method for crossing obstacles.

The experimental data and results are described and analyzed, showing that the proposed method is feasible and effective through simulation and implementation.

The main contribution is the humanoid robot’s actuation control technology and humanoid action realization, which could be used for squatting and moving heavy objects to help a humanoid robot adapt effectively.

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.

Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues.

Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup.

By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors.

Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

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.

Planning and control of humanoid biped walking has been an active research topic for many years. But, there is no definite answer to the question of how to practicre‐examinedally achieve speedy and stable walking in real‐time and in a changing environment. The purpose of this paper is to re‐examine the issue of planning and controlling humanoid biped walking, then to propose two new ideas.

The first idea is to treat the supporting foot of a biped to be part of the ground. In this way, there is a foot reaction force acting at a fixed virtual joint, which can be at, or below, the ankle joint. And, a new concept is come our that is named as in‐foot ZMP in contrast to the existing concept of on‐ground ZMP. The unique benefit with this new concept of in‐foot ZMP is that the ZMP control is no longer an issue because the in‐foot ZMP can be controlled so as to to be at a fixed virtual joint during a stable walking. Such a fixed virtual joint can be called a ZMP joint.

The second idea is to focus on hip's trajectory (instead of on‐ground ZMP's trajectory) and to split a hip's dynamic response into two independent parts: one is the steady‐state response contributing to the stability of walking (or standing), and the other is the transient response contributing to the speed of walking. This idea allows us to explicitly postulate the necessary and sufficient condition for achieving leg stability as well as the necessary and sufficient condition for achieving foot stability. The paper shows that the implementation of these two new ideas help realize a unified framework for task‐guided, intention‐guided, and sensor‐guided, planning and control of humanoid biped walking.

This paper first re‐examines the issue of planning and controlling humanoid biped walking, then proposes two new ideas. The first idea is to treat the supporting foot of a biped to be part of the ground. The second idea is to focus on hip's trajectory (instead of on‐ground ZMP's trajectory) and to split a hip's dynamic response into two independent parts: one is the steady‐state response contributing to the stability of walking (or standing), and the other is the transient response contributing to the speed of walking.