The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

This work is inspired by motion control of cleaning robots, operating in certain endogenous environments, and performing various tasks like door cleaning, wall sanitizing, etc. The base platform’s motion for these robots is generally similar to the motion of four-wheel cars. Most of the cleaning and maintenance tasks require detection, path planning, and control. The motion controller’s job is to ensure the robot follows the desired path or a set of points, pre-decided by the path planner. This control loop generally requires some feedback from the on-board sensors, and odometry modules, to compute the necessary velocity inputs for the wheels. As the sensors and odometry modules are prone to environmental noise, dead-reckoning errors, and calibration errors, the control input may not provide satisfactory performance in a closed-loop. This paper develops a robust-observer based sliding mode controller to fulfill the motion control task in the presence of incomplete state measurements and sensor inaccuracies. A robust intrinsic observer design is proposed to estimate the input matrix, which is used for dynamic feedback linearization. The resulting uncertain dynamics are then stabilized through a sliding mode controller. The proposed robust-observer based sliding mode technique assures asymptotic trajectory tracking in the presence of measurement uncertainties. Lyapunov based stability analysis is used to guarantee the convergence of the closed-loop system, and the proposed strategy is successfully validated through numerical simulations.

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For glass façade cleaning, we developed a reconfigurable robot, Mantis-mini, with a dry cleaning mechanism and linear actuator based transitioning mechanism. It consists of three suction modules, connected by a support structure and each suction module has individual differential drive wheels with independent steering capability. This paper focuses on the detailed design of the platform, static structural analysis for and kinematic formulation. The adhesion performance of the suction module is evaluated using experiments, Computational Fluid Dynamics (CFD) simulations, and analytically formulated estimation. The suction pressure magnitude predicted from the simulations increases with suction impeller speed and agrees well with measurements and analytical calculation in terms of order of magnitude and qualitative trend. An adaptive proportional-integral (PI) controller is designed and implemented for regulating suction pressure and motion controller of the platform. The suction performance of the platform is validated through real-time experiments of the platform on the glass surface.

Steam mopping is an eco-friendly solution for cleaning and disinfecting floors, avoiding harsh chemicals or harmful UV lights. Currently, commercial steam mopping systems are manually operated, which is laborious and unsafe. This work presents the design methodology of a novel, eco-friendly autonomous steam mopping robot named ‘Snail’ for cleaning and disinfecting typical indoor floors, namely tile, carpet, concrete, vinyl, sealed wood, and rugs. Our work is mainly focused on (1) the mechanical system design, including the locomotion, steam dispenser, and mopping unit, (2) the hardware and software architecture, and (3) the motion control system’s design for smooth maneuverability and mitigating friction forces. A high-gain friction estimator is proposed, whose output is exploited by the motion controller to follow the desired path and compensate for the traction-based locomotion disturbances due to extensive mop contact with a rough-textured floor, such as carpet. The proposed autonomous steam mopping robot’s efficiency is validated with different floors with zig-zagging cleaning patterns, setting a minimum of 100 °C as the disinfecting steam criterion. Using the proposed motion control algorithm, we validate the smooth maneuverability, assuring the robot traverses with a given speed and follows the trajectory even on a rough carpeted floor. The cleaning efficiency is tested by removing hard stains, consuming less water, and faster wet floor drying. The robot used only one liter of water to clean a 5 m2 floor area, and the drying time was found to be four times faster than normal water mopping. The proposed steam mopping robot design is ideal for cleaning and disinfecting hospitals and eldercare centers, where hygiene is crucial, and promoting going green, avoiding harsh chemicals, and mitigating current cleaner labor concerns, including the risk of infection.

This work presents the vision pipeline for our in-house developed autonomous reconfigurable pavement sweeping robot named Panthera. As the goal of Panthera is to be an autonomous self-reconfigurable robot, it has to understand the type of pavement it is moving in so that it can adapt smoothly to changing pavement width and perform cleaning operations more efficiently and safely. deep learning (DL) based vision pipeline is proposed for the Panthera robot to recognize pavement features, including pavement type identification, pavement surface condition prediction, and pavement width estimation. The DeepLabv3+ semantic segmentation algorithm was customized to identify the pavement type classification, an eight-layer CNN was proposed for pavement surface condition prediction. Furthermore, pavement width estimation was computed by fusing the segmented pavement region on the depth map. In the end, the fuzzy inference system was implemented by taking input as the pavement width and its conditions detected and output as the safe operational speed. The vision pipeline was trained using the DL provided with the custom pavement images dataset. The performance was evaluated using offline test and real-time field trial images captured through the reconfigurable robot Panthera stereo vision sensor. In the experimental analysis, the DL-based vision pipeline components scored 88.02% and 93.22% accuracy for pavement segmentation and pavement surface condition assessment, respectively, and took approximately 10 ms computation time to process the single image frame from the vision sensor using the onboard computer.