User-Adaptive Shared Control in a Mobility Assistance Robot based on Human-Centered Intention Reading and Decision Making Scheme

摘要

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. Here we present work on two modes of physical Human-Robot interaction; one where the human in is direct contact with the MAR, e.g. by holding some handles, and the other where the human releases the handles whilst the MAR has to follow him/her from the front, i.e. contactless. For the first mode, we present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type MAR 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 MAR is designed to provide a) cognitive assistance to help the user follow a desired path as well as b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. For the second mode, an intention-based assistive controller for allowing the robot to follow a human while moving in the front is analysed. This task is particularly challenging in indoor environments, as there are situations that are undecidable, namely in junctions. We describe a novel local kinodynamic planner which concurrently detects discrete routes and continuous motion paths. An intention recognition algorithm is also detailed, along with tests in a T-Junction.