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.

Recognizing the actions, plans, and goals of a person in an unconstrained environment is a key feature that future robotic systems will need in order to achieve a natural human-machine interaction. Indeed, we humans are constantly understanding and predicting the actions and goals of others, which allows us to interact in intuitive and safe ways. While action and plan recognition are tasks that humans perform naturally and with little effort, they are still an unresolved problem from the point of view of artificial intelligence. The immense variety of possible actions and plans that may be encountered in an unconstrained environment makes current approaches be far from human-like performance. In addition, while very different types of algorithms have been proposed to tackle the problem of activity, plan, and goal (intention) recognition, these tend to focus in only one part of the problem (e.g., action recognition), and techniques that address the problem as a whole have been not so thoroughly explored. This review is meant to provide a general view of the problem of activity, plan, and goal recognition as a whole. It presents a description of the problem, both from the human perspective and from the computational perspective, and proposes a classification of the main types of approaches that have been proposed to address it (logic-based, classical machine learning, deep learning, and brain-inspired), together with a description and comparison of the classes. This general view of the problem can help on the identification of research gaps, and may also provide inspiration for the development of new approaches that address the problem in a unified way.

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

Traveling wave ultrasonic motors are actuators relying on piezoelectric ceramics that combine many advantageous features, such as high stalling torque, fast response, compactness, and magnetic resonance compatibility. However, they suffer from nonlinear dynamics, load-dependent dead zones, and the difficulty to control low speeds. In this paper, we present a novel second-order model for traveling wave ultrasonic motors. It is based on a dry friction driving principle and features dead zone effects. Based on the model, a two-input sliding mode controller is designed. It controls both phase difference and frequency of the traveling wave, without the necessity of implementing a signum function. With this controller, the state-of-the-art is extended to the position control case, while at the same time using fine-grained phase difference control for low velocities. Moreover, we show global uniform asymptotic stability for bounded disturbances and that velocity jumps do not appear when the control domains of phase difference and frequency are switched. Finally, both the model and the controller are evaluated via simulations and experiments that include the response to a position step input under various opposing torques.

The wood industry is a basic industry supplying primary materials to produce a wide range of products. The wood processing in sawmills bridges the transformation flow of raw materials to final products using machinery. Over the last decade, the global market for wood products has become highly competitive. As a result, sawmills are continually striving to improve their efficiency throughout their production process. Recent advances in automation systems and manufacturing technologies provide traditional sawmills with an unprecedented opportunity to be transformed into automated manufacturing plants to increase their efficiency. This opportunity motivates sawmill planners to adopt decision optimization technologies for enhancing their production yields. This transformation will support sawmills to remain resilient in current and future competitive markets. Although there is a vast amount of literature on decision-making in wood manufacturing in sawmills, a comprehensive overview of related approaches and findings with a special focus on optimization of decision processes is still lacking. The review firstly focuses on exploring opportunities provided by optimizing decision-making processes in the wood manufacturing along with associated roadblocks and challenges. Then, we explore methodologies adopted in the literature when aiming for the design and implementation of optimal decision-making systems. Finally, we provide references and guidelines for researchers and manufacturers interested in the topic, as well as formulate research gaps and recommendations for future research directions.

When robots leave industrial settings, they have to be designed allowing intuitive communication with the humans they interact with. The current paper focuses on collaboration in kinesthetic tasks. Herein, we investigate decision situations. This way, the need of communication between partners can be addressed. The current paper introduces for the first time an experimental paradigm which allows studying the effect of decision making in haptic collaboration. Because reciprocal haptic feedback is challenging to provide, we analyze its efficiency in human-human collaboration to understand when it is worth to invest in this additional modality. A one degree of tracking experiment with two human partners revealed that the additional physical effort accompanying reciprocal haptic feedback is directly transformed into higher performance (compared to a control condition without reciprocal haptic feedback). Thus, the presented results motivate further research on the nature of the haptic negotiation between human partners to achieve the same performance benefits in kinesthetic collaboration with robotic partners.