Dynamics simulation of human box delivering task

Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference

Reachability Map for Diverse and Energy Efficient Stepping of Humanoids

In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the totality of robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a two-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality, resulting in optimal step positions with a 10.9% lower energy cost than those generated by linear inverted pendulum model. Despite the complexity of this nonlinear process, we found that near-minimal effort stepping locations are within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the nonuniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans

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

Gait-Behavior Optimization Considering Arm Swing and Toe Mechanisms for Biped Robot on Rough Road

芝浦工業大学2019年

Motion Planning and Control of Dynamic Humanoid Locomotion

Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot. Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d. As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end

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

Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+

Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios. Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations. In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped. Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs. Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia. The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred