Optimizing Wearable Assistive Devices with Neuromuscular Models and Optimal Control

The coupling of human movement dynamics with the function and design of wearable assistive devices is vital to better understand the interaction between the two. Advanced neuromuscular models and optimal control formulations provide the possibility to study and improve this interaction. In addition, optimal control can also be used to generate predictive simulations that generate novel movements for the human model under varying optimization criterion

Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance

© 2016, Springer Science+Business Media New York. Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted STS transfers. The validation results show a promising STS transfer success rate and overall user satisfaction

Legged Robots

International audienc

Evidence for Composite Cost Functions in Arm Movement Planning: An Inverse Optimal Control Approach

An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness

Legged Robots

International audienc

Optimization-based analysis of push recovery during walking motions to support the design of rigid and compliant lower limb exoskeletons

<p>Lower limb exoskeletons provide a promising approach to allow disabled people to walk again in the future. Designing such exoskeletons and tuning the required actuators is challenging, since the full dynamics of the combined human-exoskeleton system have to be taken into account. In particular, it is important to not only consider nominal walking motions but also extreme situations such as the recovery from large perturbations. In this paper, we present an approach based on push recovery experiments while walking, multibody system models, and least-squares optimal control to analyze the required torques to be generated by the exoskeleton, assuming that the human provides no torque. We consider seven different trials with varying push locations and push magnitudes applied on the back of the subject. In a first study, we investigate the dependency of these total joint torques on the exoskeleton mass – and compare the torques required for a human without exoskeleton to the ones for the human with two different exoskeleton configurations. In a second study, we investigate how optimally chosen passive spring-damper elements can support the required torques in the exoskeleton joints. It can be shown that the active torques can be reduced significantly in the different joints and cases.</p> <p>The picture shows a pushing experiment: The pushing person holds a stick with a force sensor at the tip. The stick and the pushed person are equipped with markers to reconstruct kinematics in motion capture experiments. The different models considered in this paper are shown. From left to right: human model, rigid combined human-exoskeleton model 1 (lighter exoskeleton), rigid combined human-exoskeleton model 2 (heavier exoskeleton), compliant combined human-exoskeleton model.</p

Open-loop Stable Control of Running Robots - A Numerical Method for Studying Stability in the Context of Optimal Control Problems

An open-loop controlled robot must have strong stability properties. This paper presents a numerical method for studying the stability of robots in the context of optimal control problems. Parameter studies for a one-legged hopping robot are performed. 1 Introduction Most running robots use sensor-based feedback to actively control the stability of their motion. Perturbations of the desired position histories are detected and appropriate counteractions are computed and performed. In the case of complex robot congurations this task can be too compute-intensive to be handled in real time. It has been shown that it is also possible to construct legged monopods and bipeds which can sustain stable dynamic locomotion without any sensors. Examples are the selfstabilizing MIT robot described by Ringrose [7] and passive dynamic walking machines, see e.g. [4]. These robots are designed in such a way that they are inherently stable and can recover automatically from small perturbations even ..