Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning

Robust Lossless Semi Fragile Information Protection in Images

Internet security finds it difficult to keep the information secure and to maintain the integrity of the data. Sending messages over the internet secretly is one of the major tasks as it is widely used for passing the message. In order to achieve security there must be some mechanism to protect the data against unauthorized access. A lossless data hiding scheme is proposed in this paper which has a higher embedding capacity than other schemes. Unlike other schemes that are used for embedding fixed amount of data, the proposed data hiding method is block based approach and it uses a variable data embedding in different blocks which reduces the chances of distortion and increases the hiding capacity of the image. When the data is recovered the original image can be restored without any distortion. Our experimental results indicate that the proposed solution can significantly support the data hiding problem. We achieved good Peak signal-to-noise ratio (PSNR) while hiding large amount of data into smoother regions

Analysis of Human Push Recovery Motions Based on Optimization

The ability to cope with large perturbations is essential to avoid falling for humans as well as for humanoid robots. Every day millions of people are affected by injuries due to falling. This is a huge problem not only for the individuum but also for the society as it costs the health care systems billions of euros. Also in the field of humanoid robots fall avoidance is very important as it protects robots against breakage. In this thesis, the problem of fall avoidance is addressed using a combination of optimization, human-modeling and recorded push recovery motions. The aim is to identify the principles that lead to human-like push recovery motions. The human is modeled by rigid segments combined by joints leading to an underactuated multi-body representation. These models are included in multiple stage optimal control problems to reconstruct and sythesize human push recovery motions considering the dynamics of a human over the whole time horizon. Due to the high nonlinearity, the optimization problem is solved based on a direct multiple shooting method. To analyze the human push recovery motions, dynamically-consistent motions for the model that closely track experimental data are produced. The joint angles and joint torques for the human model controlled by joint torque derivatives are compared for perturbed and unperturbed motions from two subjects. The results verify the assumption that the heavier the perturbation is and the higher it is applied at the upper body, the larger are the resulting joint torques. We show that including optimally chosen spring-damper elements in the joints can reduce the active joint torques significantly. We further exploit our motion reconstruction approach to determine the states that are most affected during a perturbation. Relevant parameters such as the orientation and position of the head and body, joint angles and torques of the perturbed motions are analyzed for deviations to the unperturbed motions at the point in time when the push occurs. Identifying the point in time when the model states of the perturbed motions differ from the unperturbed motions, the reaction times are determined. To better understand human push recovery motions, we also investigate in a motion sythesis approach. This approach enables a control hypothesis, in the form of a specific objective function, to be formed. The minimization of effort combined with a periodicity formulation results in human-like motions and the influence of the push strength is analyzed. Formulating the objective function as a weighted linear combination of possible optimality criteria provides the possibility to analyze different optimality criteria and their resulting motion. The difficulty is, that for a given motion, it is not known, which criteria lead to that specific motion. In this thesis, the results for different basal objective functions are analyzed. These studies prepare to determine the optimal weights of the criteria by including the presented motion generation formulation in an inverse optimal control problem. Having analyzed general weights that lead to a good approximation of the human recovery motions, the resulting objective function can be used to generate push recovery motions also for humanoid robots or assistive devices such as exoskeletons. To show another application in the improvement of technical assistive devices, we include two combined human exoskeleton models of different weights in our calculations. This allows us to analyze the joint torques for these models including the exoskeletons and compare the results to a human model. As the resulting joint torques are quite large, we also formulate combined human exoskeleton models with passive spring-damper elements that act in parallel to the active torques. This compliant formulation leads to a significant reduction of the active joint torque needed for the recovery motion. The reduction of the active joint torques allows the reduction of energy needed for the recovery motion or can enable the recovery from stronger perturbations