An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

Abstract

We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1007/s12369-016-0353-z Refereed: YesThe final publication is available at Springer via http://dx.doi.org/10.1007/s12369?016?0353?z Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.Noname manuscript No. Abstract Mobility assistance robots (MARs) 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.

Evaluating the sit‐to‐stand transfer assistance from a smart walker in older adults with motor impairments

“…The four-wheeled SW used in this study was developed in the MOBOT project ("Intelligent Active MObility Aid RoBOT integrating Multimodal Sensory Processing, Proactive Autonomy and Adaptive Interaction") and integrates innovative functionalities, such as STS assistance, obstacle avoidance, navigation assistance, user following, gait tracking and audio-gestural human-robot interaction into an overall context-aware mobility assistance robot. [14][15][16][17] The STS assistance system is based on two actuated arms providing active assistance during the entire STS motion through individualized robot handle trajectories (positions, velocities, accelerations) specifically tailored to the user's specific anthropometrics and motor impairment level. A detailed description of the STS assistance system and the optimal assistive strategies used to support the participants in the STS transfer has been provided previously.…”

Control sharing in human-robot team interaction

“…For example, in order to obtain safe and intuitive assistance, an approach to the allocation of control authority is achieved using a human-inspired decision-making model (10). The authors of [47] treat the problem of shared control of a mobile assistive robot (MAR) by solving simultaneously three low level sub-tasks: follow a path, avoid collisions and mitigate human fatigue. For each subtask a drift-diffusion decision-making model is used for gain scheduling of the low-level control parameters Sliding scale autonomy Figure 6: Block structure of the general hierarchical shared control architecture for human-robot team interaction.…”

Human-Like Motion Planning Based on Game Theoretic Decision Making

“…Moreover, artificial agents are starting to share their workspaces with humans: robots navigate autonomously through pedestrian areas (see Fig. 1) or highways [9,82,87,89]; they guide people at museums or fairs [64]; and they even have physical contact with humans and work together with them on an assembly or manipulation task [19,22] or assist the elderly [23]. All these examples have in common that their performance could be improved (e.g., readability, trustworthiness, fault tolerance, and work pace) if the navigation is human-like [10,11,13].…”