Control Of Biped Robot Research Articles

A brief review of dynamics and control of underactuated biped robots

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First Steps Towards Translating HZD Control of Bipedal Robots to Decentralized Control of Exoskeletons

This paper presents preliminary results toward translating gait and control design for bipedal robots to decentralized control of an exoskeleton aimed at restoring mobility to patients with lower limb paralysis, without the need for crutches. A mathematical hybrid dynamical model of the human-exoskeleton system is developed and a library of dynamically feasible periodic walking gaits for different walking speeds is found through nonlinear constrained optimization using the full-order dynamical system. These walking gaits are stabilized using a centralized (i.e., full-state information) hybrid zero dynamics-based controller, which is then decentralized (i.e., control actions use partial state information) so as to be implementable on the exoskeleton subsystem. A control architecture is then developed so as to allow the user to actively control the exoskeleton speed through his/her upper body posture. Numerical simulations are carried out to compare the two controllers. It is found that the proposed decentralized controller not only preserves the periodic walking gaits but also inherits the robustness to perturbations present in the centralized controller. Moreover, the proposed velocity regulation scheme is able to reach a steady state and track desired walking speeds under both, centralized, and decentralized schemes.

Robust model predictive control of biped robots with adaptive on-line gait generation

In this paper, an on-line gait control scheme is proposed for the biped robots for walking up and down the stairs. In the proposed strategy, the nonlinear model predictive control approach is used for the trajectory planning and as well as for the control of the robot. The motion of the robot is expressed in the form of a cost function and some constraints that are related to the stable walking of the robot. The main feature of this method is that it does not need any off-line trajectory planning and the walking gait is formulated such that the environmental and stability constraints of the robot are satisfied. This on-line trajectory planning gives the important ability to the robot to adjust its gait lengths. In this way, the robot is able to ascend and descend the stairs without knowing the height and depth of the stairs in advance. In the control algorithm, the Radial-Basis Function (RBF) neural network with on-line training method is used to model the behavior of the robot over the prediction horizon. The stability analysis of the closed-loop system is performed using the Lyapunov method as well as the Poincare map. The proposed method is applied to a 5-DOF biped robot in the sagittal plane. The simulation results show effectiveness of the proposed method.

Adaptive Walking Control of Biped Robots Using Online Trajectory Generation Method Based on Neural Oscillators

This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.

A Control Framework for Anthropomorphic Biped Walking Based on Stabilizing Feedforward Trajectories.

Although dynamic walking methods have had notable successes in control of bipedal robots in the recent years, still most of the humanoid robots rely on quasi-static Zero Moment Point controllers. This work is an attempt to design a highly stable controller for dynamic walking of a human-like model which can be used both for control of humanoid robots and prosthetic legs. The method is based on using time-based trajectories that can induce a highly stable limit cycle to the bipedal robot. The time-based nature of the controller motivates its use to entrain a model of an amputee walking, which can potentially lead to a better coordination of the interaction between the prosthesis and the human. The simulations demonstrate the stability of the controller and its robustness against external perturbations.

Numerical Estimation of Stability Region of Self-Clocked Bipedal Robots with Inertial Disk

Bipedal robot control techniques implementing self clocking properties, as presented in Westervelt et al. (Feedback control of dynamic bipedal robot locomotion, Taylor & Francis LLC, Milton Park, 2007) and Novaes et al. (IFAC 19th world congress, 4843–4848, 2014), have desirable properties, like the ability to drive the robot step with respect to its own geometry. In this control technique, one of the possible angular coordinates of the robot is made monotonic during the step execution. The other angular coordinates will track reference functions of the former. One difficulty with this approach is to determine the region around the nominal step for which the stability is assured. In this paper, we propose an algorithm to estimate this stability region by computing numerically an interval matrix related to a Poincare return map. In conjunction with the algorithm proposed in Ahn and Chen (IEEE Trans Autom Control 52(3):510–514, 2007), we are able to determine the maximum singular value of this interval matrix and so determine a guaranteed stability region.

Design and control of a pneumatic musculoskeletal biped robot.

Pneumatic artificial muscles are quite promising actuators for humanoid robots owing to their similar characteristics with human muscles. Moreover, biologically inspired musculoskeletal systems are particularly important for humanoid robots to perform versatile dynamic tasks. This study aims to develop a pneumatic musculoskeletal biped robot, and its controller, to realize human-like walking. According to the simplified musculoskeletal structure of human lower limbs, each leg of the biped robot is driven by nine muscles, including three pairs of monoarticular muscles which are arranged in the flexor-extensor form, as well as three biarticular muscles which span two joints. To lower cost, high-speed on/off solenoid valves rather than proportional valves are used to control the muscles. The joint trajectory tracking controller based on PID control method is designed to achieve the desired motion. Considering the complex characteristics of pneumatic artificial muscles, the control model is obtained through parameter identification experiments. Preliminary experimental results demonstrate that the biped robot is able to walk with this control strategy. The proposed musculoskeletal structure and control strategy are effective for the biped robot to achieve human-like walking.

Dynamical balance optimization and control of biped robots in double-support phase under perturbing external forces

To realize the dynamic balance optimization and control of biped robots under the perturbing external forces in the double-support phase, a systematic scheme is proposed in this paper. First, a constrained dynamic model of biped robots and a reduced order dynamical model for the double-support phase are formulated. Considering the dynamic external wrench applied on biped robots, we present a dynamic force distribution approach based on quadratic objective function for computing the optimal contact forces to equilibrate the dynamic external wrench. As a result, the sum of the normal force components is minimized for enhancing safety and energy saving. Then, one primary recurrent neural network (RNN) is adopted to solve the optimization problem subject to both equality and inequality constraints. For the derived optimized contact force and motion, hybrid motion/force control is proposed based on another RNN to approximate unknown dynamic functions. Adaptive learning algorithms for learning the parameters of the RNN are provided as well. The proposed control can deal with the uncertainties including approximation errors and external disturbances. Extensive simulations are presented to demonstrate the effectiveness of the proposed optimization and control approach.

Energy-efficient Bio-inspired Gait Planning and Control for Biped Robot Based on Human Locomotion Analysis

In this paper an experiment of human locomotion was carried out using a motion capture system to extract the human gait features. The modifiable key gait parameters affecting the dominant performance of biped robot walking were obtained from the extracted human gait features. Based on the modifiable key gait parameters and the Allowable Zero Moment Point (ZMP) Variation Region (AZR), we proposed an effective Bio-inspired Gait Planning (BGP) and control scheme for biped robot towards a given travel distance D. First, we construct an on-line Bio-inspired Gait Synthesis algorithm (BGSN) to generate a complete walking gait motion using the modifiable key gait parameters. Second, a Bio-inspired Gait Parameters Optimization algorithm (BGPO) is established to minimize the energy consumption of all actuators and guarantee biped robot walking with certain walking stability margin. Third, the necessary controllers for biped robot were introduced in briefly. Simulation and experiment results demonstrated the effectiveness of the proposed method, and the gait control system was implemented on DRC-XT humanoid robot.

Torque-Stiffness-Controlled Dynamic Walking: Analysis of the Behaviors of Bipeds with Both Adaptable Joint Torque and Joint Stiffness

This article presents a general paradigm, torque-stiffness-controlled dynamic walking, to analyze the behaviors of bipeds with both adaptable joint torque and joint stiffness. A bioinspired control method with central pattern generators (CPGs) is applied to realize limb coordination and smooth walking pattern transitions. To better illustrate the proposed paradigm, we show the respective effects of joint torque and joint stiffness on walking performance and motion control in both a four-link simulated model and a robot prototype. This study may develop a new solution for the motion control of bipedal robots with adaptable joint stiffness and provide insights into the principles of efficient and adaptive human walking.

着地時の立位安定性を保証する二脚ロボットの運足誘導型制御

A new biped robot control enables a robust foot-guided maneuver which suits to the transportation in complex environments. The standing stability at the moment of landing is guaranteed based on the model-predictive control. It is potentially flexible against perturbations since it only requires the current COM position and velocity with respect to the pivot foot.

Neural Network Reinforcement Learning for Walking Control of a 3-Link Biped Robot

In recent years, researches on adaptive control have focused on bio-inspired learning techniques to deal with real-life applications. Reinforcement Learning (RL) is one of these major techniques, which has been widely used in robot control approaches. The implementation of artificial neural networks in RL algorithms enables more efficient optimal control strategies. This article proposes a field application of neural network reinforcement learning (NNRL) for walking control of an active simulated 3-link biped robot. The adaptive control agent consists of two neural network units, known as actor and critic for learning prediction and learning control tasks. Results of the presented control method reveal its efficiency in stable walking control of the biped robot model as a nonlinear complex dynamic task.

Energy-Efficient Gait Planning and Control for Biped Robots Utilizing Vertical Body Motion and Allowable ZMP Region

An energy-efficient gait planning (EEGP) and control system is established for biped robots with three-mass inverted pendulum mode (3MIPM), which utilizes both vertical body motion (VBM) and allowable zero-moment-point (ZMP) region (AZR). Given a distance to be traveled, we newly designed an online gait synthesis algorithm to construct a complete walking cycle, i.e., a starting step, multiple cyclic steps, and a stopping step, in which: 1) ZMP was fully manipulated within AZR; and 2) vertical body movement was allowed to relieve knee bending. Moreover, gait parameter optimization is effectively performed to determine the optimal set of gait parameters, i.e., average body height and amplitude of VBM, number of steps, and average walking speed, which minimizes energy consumption of actuation motors for leg joints under practical constraints, i.e., geometrical constraints, friction force limit, and yawing moment limit. Various simulations were conducted to identify the effectiveness of the proposed method and verify energy-saving performance for various ZMP regions. Our control system was implemented and tested on the humanoid robot DARwIn-OP.

Dead reckoning for biped robots that suffers less from foot contact condition based on anchoring pivot estimation

A novel technique of dead reckoning for high-rate feedback control of biped robots is proposed. A fast position estimation of a robot is achieved by fusing information only from internal sensors including joint angle encoders, inertial sensors and force sensors. It combines the kinematics computation and the double integral of acceleration in a complementary way in order to improve the accuracy. The kinematics computation takes the movement of supporting foot, particularly, rotation about a fixed point and rolling on the terrain into consideration. The weights on each information are adjusted automatically based on the reaction force from the ground as it is expected to reflect the certainty of the contact condition of each foot. The validity of the proposed method is verified through computer simulations.

Multi-level control of zero-moment point-based humanoid biped robots: a review

SUMMARYResearchers dream of developing autonomous humanoid robots which behave/walk like a human being. Biped robots, although complex, have the greatest potential for use in human-centred environments such as the home or office. Studying biped robots is also important for understanding human locomotion and improving control strategies for prosthetic and orthotic limbs. Control systems of humans walking in cluttered environments are complex, however, and may involve multiple local controllers and commands from the cerebellum. Although biped robots have been of interest over the last four decades, no unified stability/balance criterion adopted for stabilization of miscellaneous walking/running modes of biped robots has so far been available. The literature is scattered and it is difficult to construct a unified background for the balance strategies of biped motion. The zero-moment point (ZMP) criterion, however, is a conservative indicator of stabilized motion for a class of biped robots. Therefore, we offer a systematic presentation of multi-level balance controllers for stabilization and balance recovery of ZMP-based humanoid robots.

Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generators

A closed-loop system for the central pattern generator (CPG)-based locomotion control of biped robots that operates in the joint space is presented. The proposed system has been designed to allow biped robots to walk on unknown sloped surfaces. Feedback signals generated by the robot's inertial and force sensors are directly fed into the CPG to automatically adjust the locomotion pattern over flat and sloped terrain in real time. The proposed control system negotiates sloped surfaces similarly to state-of-the-art CPG-based control systems; however, whereas the latter must continuously solve the computationally intensive inverse kinematics of the robot, the proposed approach directly operates in the joint space, which makes it especially suitable for direct hardware implementation with electronic circuits. The performance of the proposed control system has been assessed through both simulation and real experiments on a NAO humanoid robot.

Circular Obstacle Avoidance Control of the Compass-Type Biped Robot Based on a Blending Method of Discrete Mechanics and Nonlinear Optimization

This paper considers an obstacle avoidance control problem for the compass-type biped robot, especially circular obstacles are dealt with. First, a sufficient condition such that the swing leg does not collide the circular obstacle is derived. Next, an optimal control problem for the discrete compass-type robot is formulated and a solving method of the problem by the sequential quadratic programming is presented in order to calculate a discrete control input. Then, a transformation method that converts a discrete control input into a continuous zero-order hold input via discrete Lagrange-d’ Alembert principle is explained. From the results of numerical simulations, it turns out that obstacle avoidance control for the continuous compass-type robot can be achieved by the proposed method.

Torque-stiffness-controlled dynamic walking with central pattern generators.

Walking behavior is modulated by controlling joint torques in most existing passivity-based bipeds. Controlled Passive Walking with adaptable stiffness exhibits controllable natural motions and energy efficient gaits. In this paper, we propose torque-stiffness-controlled dynamic bipedal walking, which extends the concept of Controlled Passive Walking by introducing structured control parameters and a bio-inspired control method with central pattern generators. The proposed walking paradigm is beneficial in clarifying the respective effects of the external actuation and the internal natural dynamics. We present a seven-link biped model to validate the presented walking. Effects of joint torque and joint stiffness on gait selection, walking performance and walking pattern transitions are studied in simulations. The work in this paper develops a new solution of motion control of bipedal robots with adaptable stiffness and provides insights of efficient and sophisticated walking gaits of humans.

Balance control of planar biped robots using virtual holonomic constraints

SUMMARYThe balance control problem of planar bipedal robots during disturbed standing is investigated. Virtual holonomic constraints which specify the angles of actuated joints as a function of the rotation angle between the sole of the stance foot and the ground are recalled. These constraints enable the robot to avoid turnover during external disturbances similar to how a human being would react. Moreover, for a disturbance beyond the balance conditions for single-leg support, the robot changes to a double support posture to avoid turnover by the virtual holonomic constraints. Further, for a special kind of balance: standing on toe which leads to underactuation, we also give the design conditions of the virtual holonomic constraints.

Energy-Efficient Gait Planning and Control for Biped Robots Utilizing the Allowable ZMP Region

Energy-efficient gait planning and control is established for biped robots, which utilizes the allowable zero moment point (ZMP) region. Based on 3-D linear inverted pendulum mode (LIPM), we construct a practical gait planning algorithm for a given travel distance minimizing the energy consumed by the actuators of humanoid joints with 1) an online gait synthesis (GSYN) algorithm to generate a complete walking cycle (a starting step, several cyclic steps, and a stopping step) compromising waking stability and energy efficiency at the fully utilizing allowable ZMP region and with 2) effective gait parameter optimization to maximize the energy efficiency of the gaits generated by GSYN, finding two optimal parameters-number of steps and average walking speed-satisfying geometrical constraints, friction force limit, and yawing moment limit to guarantee feasible motions. The proposed algorithm was verified through simulations, and the gait control system was implemented on a DARwIn-OP humanoid robot.