The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented. The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro. An autonomy algorithm is also proposed for self-reconfiguration, locomotion, and autonomous navigation of sTetro in the controlled environment, for example, in homes/offices with a flat floor and a straight staircase. A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.
Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.
The design of cleaning and maintenance (CaM) robots is generally limited by their fixed morphologies, resulting in limited functions and modes of operation. Contrary to fixed shape robots, the design of reconfigurable robots presents unique challenges in designing their system, subsystems, and functionalities with the scope for innovative operational scenarios and achieving high performance in multiple modalities without compromise. This paper proposes a heuristic framework using three layers, namely input, formulation, and output layer, for designing reconfigurable robots with the aid of established transformation principles including expand/collapse, expose/cover, and fuse/divide observed in several products, services, and systems. We apply this heuristic framework approach to the novel design of a pavement CaM robotic system and subsystems, namely, i) Varying footprint, ii) Transmission, iii) Outer skin or cover iv) Storage bin, v) Surface cleaning, and vi) Vacuum/suction and blowing. The advances in the design method using the heuristic approach are demonstrated by developing an innovative reconfigurable design for CaM task. Kinematic analysis and control architecture enabling the unique locomotion behavior and gaits, namely, a) static reconfiguration and b) reconfiguration while locomotion, supported by the control architecture. Experiments were conducted and outcomes are discussed along with the failure mode analysis to support the design robustness and limitations through the observations made from the development to testing phase over one year. A detailed video demonstrating the design capabilities is linked.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.