Wall cleaning robots are developed to cater to the demands of the building maintenance sector. The ability to climb vertical surfaces is one of the crucial requirements of a wall cleaning robot. Robots that can climb vertical surfaces by adhesion to a surface are preferred since those do not require additional support structures. Vacuum suction mechanisms are widely used in this regard. The suction force acting on the robot due to the negative pressure built up is used by these robots for the adhesion. A robot will fall off or overturn when the pressure difference drops down a certain threshold. In contrast, if the pressure difference becomes too high, the excessive amount of frictional forces will hinder the locomotion ability. Moreover, a wall cleaning robot should be capable of adapting the adhesion force to maintain the symmetry between safe adhesion and reliable locomotion since adhesion forces which are too low or too high hinder the safety of adhesion and reliability of locomotion respectively. Thus, the pressure difference needs to be sustained within a desired range to ensure a robot’s safety and reliability. However, the pressure difference built up by a vacuum system may unpredictably vary due to unexpected variation of air leakages due to irregularities in surfaces. The existing wall cleaning robots that use vacuum suction mechanisms for adhesion are not aware of the adhesion status, or subsequently responding to them. Therefore, this paper proposes a design for a wall cleaning robot that is capable of adapting vacuum power based on the adhesion-awareness to improve safety and reliability. A fuzzy inference system is proposed here to adapt the vacuum power based on the variation of the adhesion and the present power setting of the vacuum. Moreover, an application of fuzzy logic to produce a novel controlling criterion for a wall cleaning robot to ensure safety and reliability of operation is proposed. A fuzzy inference system was used to achieve the control goals, since the exact underlying dynamics of the vacuum-adhesion cannot be mathematically modeled. The design details of the robot are presented with due attention to the proposed control strategy. Experimental results confirmed that the performance of a robot with proposed adhesion-awareness surpasses that of a robot with no adhesion-awareness in the aspects of safety, reliability, and efficiency. The limitations of the work and future design suggestions are also discussed.

The autonomous floor-cleaning self-reconfigurable robots have entered into the practical stage by establishing enhanced area coverage over the fixed morphology counterparts. Energy consumption during the self-reconfiguration, i.e., changing the shape of the robot from one form to another, becomes a primary focus in these robots that carry finite energy sources. In this paper, hTetro platform with two hinge dissections namely, Left–Left–Left (LLL) and Left–Left–Right (LLR) are modeled and assessed for the energy consumption during the reconfiguration. The geometry of the two dissections, its workspaces, and the set of inverse kinematics solutions for the seven forms are presented. The inverse dynamics using the Newton–Euler approach was adopted to calculate the wrench, i.e., forces and moments at the hinge joints, and subsequently assess the power consumed during the reconfigurations of two hinge dissections in the simulation. Extensive experiments were performed across the two assembled platforms to estimate the power consumption by logging the current data. The comparison was made with the simulation results. The results are particularly useful in the selection of reconfiguration with minimal energy consumption during the floor cleaning.

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

Background and Objective: Semantic inhibition — the ability to suppress prepotentresponses in favour of goal-directed behaviour — is a core component of cognitivecontrol. The N450 event-related potential is a well-established marker of semanticconflict in tasks such as the Hayling Sentence Completion Test. However, traditionalanalyses reduce this component to a mean amplitude measure, providing limitedinsight into its underlying neural dynamics. The present study re-analyses an existingelectroencephalography dataset using a novel computational framework tocharacterize the dynamical and informational structure of the N450 response.Methods: Single-trial electroencephalography segments aligned to the N450 peakwere modeled using a Dual Damped Sine framework, which captures transientresponses as the superposition of two damped oscillatory modes. Information-theoretic metrics — Active Information Storage and Transfer Entropy — were thenapplied to the residual signal after removing the modeled oscillatory component, toquantify temporal persistence and directed information transfer across corticalregions of interest. Analyses were conducted on 17,496 trial-level fits from 47participants performing the Hayling task. Primary contrasts were corrected formultiple comparisons using the false discovery rate procedure.Results: The Dual Damped Sine model captured the substantial temporal structureof single-trial responses, with a median goodness-of-fit of approximately 0.56 forvalid fits. Residual analyses revealed a systematic redistribution of informationdynamics within the fronto-cingulate network during semantic inhibition. ActiveInformation Storage decreased in lateral frontal cortex and simultaneously increasedin the anterior cingulate cortex, while directed information transfer from frontal cortexto the anterior cingulate showed a modest but significant reduction. All three primarycontrasts survived false discovery rate correction.Conclusions: The N450 reflects a transient reconfiguration of fronto-cingulateinformation dynamics rather than a simple amplitude deflection. Semantic inhibitionis associated with a shift from distributed lateral frontal representations toward locallyrecurrent medial frontal dynamics. Integrating dynamical system modeling withinformation-theoretic analysis enables a richer characterization of cognitive control,capturing not only response magnitude but the evolving organization of informationprocessing in the brain. A fully reproducible analysis pipeline is publicly available.