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

Personal navigation via high-resolution gait-corrected inertial measurement units

Journal ArticleAbstract-In this paper, a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units is presented. The goal of this paper is to develop a navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zerovelocity duration from the ground reaction sensors is used to reset the accumulated integration errors from accelerometers and gyroscopes in position calculation. With the described system, an average position error of 4 m is achieved at the end of half-hour walks

Personal navigation via shoe mounted inertial measurement units

Journal ArticleWe are developing a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units. The goal of this project is to develop a navigation system that use secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero velocity durations from the ground reaction sensors are used to reset the accumulated integration errors from the accelerometers and gyroscopes in position calculation. We achieved an average position error of 4 meters at the end of half-hour walks

A Soft+Rigid Hybrid Exoskeleton Concept in Scissors-Pendulum Mode: A Suit for Human State Sensing and an Exoskeleton for Assistance

In this paper, we present a novel concept that can enable the human aware control of exoskeletons through the integration of a soft suit and a robotic exoskeleton. Unlike the state-of-the-art exoskeleton controllers which mostly rely on lumped human-robot models, the proposed concept makes use of the independent state measurements concerning the human user and the robot. The ability to observe the human state independently is the key factor in this approach. In order to realize such a system from the hardware point of view, we propose a system integration frame that combines a soft suit for human state measurement and a rigid exoskeleton for human assistance. We identify the technological requirements that are necessary for the realization of such a system with a particular emphasis on soft suit integration. We also propose a template model, named scissor pendulum, that may encapsulate the dominant dynamics of the human-robot combined model to synthesize a controller for human state regulation. A series of simulation experiments were conducted to check the controller performance. As a result, satisfactory human state regulation was attained, adequately confirming that the proposed system could potentially improve exoskeleton-aided applications

A gait adaptation scheme for biped walking robots

Recent years witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control requires many problems to be solved because of the many degrees of freedom and nonlinearity in their dynamics. The so-called open loop walking with offline trajectory generation is one of the control approaches in the literature. There are various difficulties involved in this approach, the most important one being the difficulty in tuning the gait parameters. This paper proposes an online fuzzy adaptation scheme for one of the trajectory parameters in the offline generated walking pattern. A fuzzy identifier system, represented as a three-layer feed-forward neural network is employed to compute the parameter as a function of time in simulations. Fuzzy system parameters are adapted via back-propagation. Virtual torsional springs are attached to the trunk center of the biped. The torque generated by the springs serve as the criterion for the tuning and they help maintaining a stable and a longer walk which is necessary for the online tuning process. 3D simulation and animation techniques are employed for a 12-DOF biped robot to test the proposed adaptive method

Whisker Sensor Design for Three Dimensional Position Measurement in Robotic Assisted Beating Heart Surgery

Abstract — In the robotic-assisted off-pump Coronary Artery Bypass Graft (CABG) surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation that replace conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart. Measuring the motion of the heart during this operation is an important part of this scheme. In this paper, a novel whisker sensor design to measure the heart motion in three dimensions (3-D) is presented. The proposed whisker sensor is a flexible contact sensor. Low stiffness of the sensor prevents damage on the tissue it contacts. This paper explains the design concept, and reports the simulation and measurement results of the prototype whisker position sensor. I

M.: Predictive control algorithms using biological signals for active relative motion canceling in robotic assisted heart surgery

Abstract — Robotics technology promises an enhanced way of performing off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation in place of conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point-of-interest on the beating heart, in contrast to traditional off-pump CABG where the heart is passively constrained to dampen the beating motion. As a result, the surgeon operates on the heart as if it were stationary. This algorithm is called Active Relative Motion Canceling (ARMC). In this paper, the use of biological signals, such as electrocardiogram (ECG), to achieve better motion canceling in the model-based intelligent ARMC algorithm is proposed. An ECG contains records for the electrical activity of the heart, which forms a series of waves and complexes. Real time identification of these waves and complexes will improve the estimation of the future heart motion and improve the performance of the ARMC algorithm. Finally, the experimental results of the algorithm implemented on a 3-DOF robotic test-bed system are reported. I

Visual needle tip tracking in 2D US guided robotic interventions

Percutaneous needle procedures are among the most frequently performed minimally invasive surgical procedure. For tracking the needle tip in the tissue, 2D ultrasound (US) imaging is commonly used; however, the low resolution of the images creates a challenge for tracking. This paper describes a robotic system that can perform US image guided biopsies by tracking the needle and the target simultaneously. It uses a template-based visual tracking method for small and deformable targets. During the experiments, a needle was inserted into realistic phantoms using a 5-DOF robot. The 2D US probe was held by a robotic arm that was servoed along the needle path. The 3D shape of the needle was estimated using the 2D transverse US images, which was used to align the needle axis with the 2D imaging plane. The accuracy of the visual needle tip tracking was evaluated using an optical tracking system, and a computed tomography scanner was used to determine the accuracy of the 3D needle shape estimation method. Target reaching accuracies were measured using an electromagnetic tracking system. The results of the experiments showed that the proposed system can track the needle tip in 2D US guided needle procedures in real-time with a sub-millimeter positional error