Stokesian jellyfish: Viscous locomotion of bilayer vesicles

Motivated by recent advances in vesicle engineering, we consider theoretically the locomotion of shape-changing bilayer vesicles at low Reynolds number. By modulating their volume and membrane composition, the vesicles can be made to change shape quasi-statically in thermal equilibrium. When the control parameters are tuned appropriately to yield periodic shape changes which are not time-reversible, the result is a net swimming motion over one cycle of shape deformation. For two classical vesicle models (spontaneous curvature and bilayer coupling), we determine numerically the sequence of vesicle shapes through an enthalpy minimization, as well as the fluid-body interactions by solving a boundary integral formulation of the Stokes equations. For both models, net locomotion can be obtained either by continuously modulating fore-aft asymmetric vesicle shapes, or by crossing a continuous shape-transition region and alternating between fore-aft asymmetric and fore-aft symmetric shapes. The obtained hydrodynamic efficiencies are similar to that of other low Reynolds number biological swimmers, and suggest that shape-changing vesicles might provide an alternative to flagella-based synthetic microswimmers

Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima

The optimization of three problems with high dimensionality and many local minima are investigated under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO package, and QNSTOP, a new algorithm developed at Indiana University

An investigation of reach decisions during ongoing action control

Les études neurophysiologiques de la prise de décision, traditionnellement ancrées dans des principes neuro-économiques, ont évoluées pour inclure une variété d’aires du cerveau. Partant d’abord du lobe frontal associé aux jugements de valeur, le champ s’est élargi pour inclure d’autres types de décisions incluant les décisions perceptuelles et les décisions incarnées qui impliquent notamment les aires sensorimotrices du cerveau. La théorie moderne de la prise de décision modèle l’activité neurale dans ces régions comme une compétition entre les différents stimuli et actions considérés par un individu. Cette compétition est résolue lorsque l’activité neurale associée à un stimulus ou une action choisie atteint un seuil critique. Toutefois, il reste à éclaircir comment ce modèle s’applique aux décisions effectuées alors que l’individu est déjà engagé dans une activité. Dans ce mémoire nous examinons ce type de décision chez des sujets humains dans une tâche de suivi continu. Des cibles « choix » apparaissaient sur un écran pendant que le sujet suivait de la main une cible qui se déplaçait doucement en continu. Le sujet pouvait ignorer ces cibles choix, ou abandonner la cible suivie pour toucher une cible choix, dans quel cas la cible sélectionnée devenait la nouvelle cible à suivre du doigt. Tel qu’attendu, nous avons observé que les sujets favorisaient les cibles plus proches, plus grandes, et les cibles alignées avec l’axe du mouvement. Toutefois nous avons été surpris de constater que les sujets ignoraient les coûts énergétiques du mouvement, tel que modélisés. Un biais pour minimiser les coûts du mouvement fut réintroduis lorsque la tâche fut divisée en séries de mouvements point-à-point, plutôt qu’un mouvement continu. Même si nous ne pouvons expliquer ce résultat surprenant, nous espérons qu’il inspire de futures études utilisant le paradigme expérimental de décision durant l’action.Neurophysiological studies of decision-making have expanded over decades to involve many brain areas. The field broadened from neuroeconomics, mainly concerned with frontal regions, to perceptual or embodied decision-making involving several sensorimotor areas where neural activity is linked to the stimuli and actions necessary for the decision process. Current models of decision-making envision this neural activity as a competition between different actions that is resolved when enough activity favors one over the other. However, it is unclear how such models can explain decisions often present in natural behavior, where deliberation takes place while already engaged in an action. In this thesis, we examined the choices human subjects made as they were engaged in a continuous tracking task. While they were manually tracking a target on a flat screen, subjects were occasionally presented with a new target to which they could freely choose to switch, whereupon it became the new tracked target. As expected, we found that subjects were more likely to move to closer targets, bigger targets, or targets that were aligned to the direction of movement. However, we were surprised that subjects did not choose targets that minimized energetic cost, as calculated by a biomechanical model of the arm. A biomechanical bias was restored when the continuous movement was broken up into a series of point to point movements. While we cannot yet explain these findings with certainty, we hope they will inspire further studies using decide-while-acting paradigms

Organizing principles underlying the formation of arm trajectories

Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology Program in Medical Engineering and Medical Physics, 1983.MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE.Bibliography: leaves 200-214.by Tamar Yashin-Flash.Ph.D

Optimal gait and form for animal locomotion

We present a fully automatic method for generating gaits and morphologies for legged animal locomotion. Given a specific animal’s shape we can determine an efficient gait with which it can move. Similarly, we can also adapt the animal’s morphology to be optimal for a specific locomotion task. We show that determining such gaits is possible without the need to specify a good initial motion, and without manually restricting the allowed gaits of each animal. Our approach is based on a hybrid optimization method which combines an efficient derivative-aware spacetime constraints optimization with a derivative-free approach able to find non-local solutions in high-dimensional discontinuous spaces. We demonstrate the effectiveness of this approach by synthesizing dynamic locomotions of bipeds, a quadruped, and an imaginary five-legged creature

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Differential correction methods in spacecraft attitude determination

Differential correction methods in spacecraft attitude determinatio

Enhancing fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones

Industrial collaborative robotics is promising for manufacturing activities where the presence of a robot alongside a human operator can improve operator’s working conditions, flexibility, and productivity. A collaborative robotic application has to guarantee not only safety of the human operator, but also fluency in the collaboration, as well as performance in terms of productivity and task time. In this paper, we present an approach to enhance fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones. A supervisory controller runs online safety checks between bounding volumes enclosing robot and human to identify possible collision dangers. To optimize the sizes of safety zones enclosing the manipulator, the method minimizes the time of potential stop trajectories considering the robot dynamics and its torque constraints, and leverages the directed speed of the robot parts with respect to the human. Simulations and experimental tests on a seven-degree-of-freedom robotic arm verify the effectiveness of the proposed approach, and collaborative fluency metrics show the benefits of the method with respect to existing approaches

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

Instantaneous Momentum-Based Control of Floating Base Systems

In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption -- typical of fixed base robots -- of having one link always attached to the ground. From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot's momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control. This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions