Development of an analytical framework for robot-inclusive homes, and of an autonomous assistive robot

Abstract

Robotic assistance in domestic environments is gaining importance for supporting older adults and people with impairments. Many domestic robots depend on complex algorithms and high computational power, which raises cost and makes integration into ordinary homes difficult. At the same time, most homes are not arranged to help robots see, reach, or move well. This research addresses that mismatch by treating the person, the robot, and the home as one measurable system, and by showing that a simple robot can deliver useful assistance in a space designed to support it. The research develops a Robot-Inclusive Space framework that formalises design and evaluation across four linked measures. Human Impairment Index evaluation and task demand set an explicit upper bound on robot capability through the Robot Complexity Index. The Robot Inclusive Space Index quantifies how layout choices support observability, accessibility, and manipulability. The Space Convertibility Index estimates the effort to reach a target layout under practical limits of cost, effort, and time. The study follows a structured RIS workflow: impairments are evaluated and translated into remaining capacities, mapped to task demands, and used to identify the minimum robot features required. RIS home modifications are then defined to support both robot operation and human needs, and their feasibility is assessed in terms of cost, effort, and time. Using a teleoperated baseline, MARIS-I, the study motivates MARISII, a semi-autonomous platform sized for small homes that combines lightweight mapping, marker-aided localisation, goal-biased curvature-bounded planning, and suitable sensor placement with simple task stations consistent with the framework. Validation in physical layouts designed according to Robot-Inclusive Space principles uses consistent hardware and repeatable trials to assess reliability and efficiency. Clear sightlines, uncluttered paths, structured object placement, and marker cues keep mapping and planning lightweight and support pick and place within defined zones. Across experiments, RIS-guided design reduces planning latency, turn counts, and processor load while maintaining path quality, enabling MARIS-II to operate more reliably with lower computation. Overall, the results show that measurable improvements in observability, accessibility and manipulability reduce the need for robot-side complexity and help identify the smallest set of feasible home changes, within realistic cost, effort and time limits, for compact single-floor homes.