A study on automatic gait parameter tuning for biped walking robots

Automatic gait parameter tuning for biped walking robots is the subject of this thesis. The biped structure is one of the most versatile ones for the employment of mobile robots in the human environment. Their control is challenging because of their many DOFs and nonlinearities in their dynamics. Open loop walking with offline walk pattern generation is one of the methods for walking control. in this method the reference positions of the foot centers with respect to the body center are generated as functionals. Commonly, the tuning process for the trajectory generation is based on numerous trial and error steps. Obviously, this is a time consuming and elaborate process. In this work, online adaptation schemes for one of the trajectory parameters, "z-reference asymmetry", which is used for the compensation of uneven weight distribution of the robot in the sagittal plane, is proposed. In one of the approaches presented, this parameter is tuned online. As an alternative to parameter tuning, a functional learning scheme employing fuzzy identifiers is tested too. Fuzzy identifiers are universal function approximators. Fuzzy system parameters are adapted via back-propagation. An on-line tuning scheme for biped walk parameters however can only be successful if there is sufficient time for training without falling. The training might last hundreds of reference cycles. This implies that a mechanism for keeping the robot in continuous walk, even when the parameter settings are totally wrong, is necessary during training. In this work, virtual torsional springs which resist against deviations of the robot trunk angles from zero, are attached to the trunk center of the biped. The torques generated by the springs serve as the criteria for the tuning and help in maintaining a stable and a longer walk. The springs are removed after training. This novel approach can be applied to a wide range of control systems that involve parameter tuning. 3-D simulation techniques using C++ are employed for the model of a 12-DOF biped robot to test the proposed adaptive method. in order to visualize the walking, simulation results are animated using an OpenGL based animation environment. As a result of the simulations, a functional for the desired parameter, keeping the system in balance while walking, is generated

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Bipedal humanoid robot walking reference tuning by the use of evolutionary algorithms

Various aspects of humanoid robotics attracted the attention of researchers in the past four decades. One of the most challenging tasks in this area is the control of bipedal locomotion. The dynamics involved are highly nonlinear and hard to stabilize. A typical fullbody humanoid robot has more than twenty joints and the coupling effects between the links are significant. Reference generation plays a vital role for the success of the walking controller. Stability criteria including the Zero Moment Point (ZMP) criterion are extensively applied for this purpose. However, the stability criteria are usually applied on simplified models like the Linear Inverted Pendulum Model (LIPM) which only partially describes the equations of the motion of the robot. There are also trial and error based techniques and other ad-hoc reference generation techniques as well. This background of complicated dynamics and difficulties in reference generation makes automatic gait (step patterns of legged robots) tuning an interesting area of research. A natural command for a legged robot is the velocity of its locomotion. A number of walk parameters including temporal and spatial variables like stepping period and step size need to be set properly in order to obtain the desired speed. These problems, when considered from kinematics point of view, do not have a unique set of walking parameters as a solution. However, some of the solutions can be more suitable for a stable walk, whereas others may lead to instability and cause robot to fall. This thesis proposes a gait tuning method based on evolutionary methods. A velocity command is given as the input to the system. A ZMP based reference generation method is employed. Walking simulations are performed to assess the fitness of artificial populations. The fitness is measured by the amount of support the simulated bipedal robot received from torsional virtual springs and dampers opposing the changes in body orientation. Cross-over and mutation mechanisms generate new populations. A number of different walking parameters and fitness functions are tested to improve this tuning process. The walking parameters obtained in simulations are applied to the experimental humanoid platform SURALP (Sabanci University ReseArch Labaratory Platform). Experiments verify the merits of the proposed reference tuning method

Biped dynamic walking using reinforcement learning

This thesis presents a study of biped dynamic walking using reinforcement learning. A hardware biped robot was built. It uses low gear ratio DC motors in order to provide free leg movements. The Self Scaling Reinforcement learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. A new learning architecture was designed to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of modules that represent new knowledge, or new requirements for the desired task. Control experiments were carried out using a simulator and the physical biped. The biped learned dynamic walking on flat surfaces without any previous knowledge about its dynamic model

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

A Foot Placement Strategy for Robust Bipedal Gait Control

This thesis introduces a new measure of balance for bipedal robotics called the foot placement estimator (FPE). To develop this measure, stability first is defined for a simple biped. A proof of the stability of a simple biped in a controls sense is shown to exist using classical methods for nonlinear systems. With the addition of a contact model, an analytical solution is provided to define the bounds of the region of stability. This provides the basis for the FPE which estimates where the biped must step in order to be stable. By using the FPE in combination with a state machine, complete gait cycles are created without any precalculated trajectories. This includes gait initiation and termination. The bipedal model is then advanced to include more realistic mechanical and environmental models and the FPE approach is verified in a dynamic simulation. From these results, a 5-link, point-foot robot is designed and constructed to provide the final validation that the FPE can be used to provide closed-loop gait control. In addition, this approach is shown to demonstrate significant robustness to external disturbances. Finally, the FPE is shown in experimental results to be an unprecedented estimate of where humans place their feet for walking and jumping, and for stepping in response to an external disturbance

Walking trajectory generation & control of the humanoid robot: suralp

In recent years, the operational area of the robots started to extend and new functionalities are planned for them in our daily environments. As the human-robot interaction is being improved, the robots can provide support in elderly care, human assistance, rescue, hospital attendance and many other areas. With this motivation, an intensive research is focused around humanoid robotics in the last four decades. However, due to the nonlinear dynamics of the robot and high number of degrees of freedom, the robust balance of the bipedal walk is a challenging task. Smooth trajectory generation and online compensation methods are necessary to achieve a stable walk. In this thesis, Cartesian foot position references are generated as periodic functions with respect to a body-fixed coordinate frame. The online adjustment of these parameterized trajectories provides an opportunity in tuning the walking parameters without stopping the robot. The major contribution of this thesis in the context of trajectory generation is the smoothening of the foot trajectories and the introduction of ground push motion in the vertical direction. This pushing motion provided a dramatic improvement in the stability of the walking. Even though smooth foot reference trajectories are generated using the parameter based functions, the realization of a dynamically stable walk and maintenance of the robot balance requires walking control algorithms. This thesis introduces various control techniques to cope with disturbances or unevenness of the walking environment and compensate the mismatches between the planned and the actual walking based on sensory feedback. Moreover, an automatic homing procedure is proposed for the adjustment of the initial posture before the walking experiments. The presented control algorithms include ZMP regulation, foot orientation control, trunk orientation control, foot pitch torque difference compensation, body pitch angle correction, ground impact compensation and early landing modification. The effectiveness of the proposed trajectory generation and walking control algorithms is tested on the humanoid robot SURALP and a stable walk is achieved