Applying the automation in covering the areas entirely eases manual jobs in various domestic fields such as site investigation, search, rescue, security, cleaning, and maintenance. A self-reconfigurable robot with adjustable dimensions is a viable answer to improve the coverage percentage for predefined map areas. However, the shape-shifting of this robot class also adds to the complexity of locomotion components and the need for an optimal complete coverage strategy for this new type of robot. The typical complete coverage route, including the least times of shape-shifting, the shortest navigation route, and the minimum travel time, is presented in the article. By splitting the map into the sub-areas similar to the self-reconfigurable robot's available shapes, the robot can design the ideal tileset and optimal navigation strategies to cover the workspace. To this end, we propose a Complete Tileset Energy-Aware Coverage Path Planning (CTPP) framework for a tiling self-reconfigurable robot named hRombo with four rhombus-shaped modules. The robot can reconfigure its base structure into seven distinct forms by activating the servo motors to drive the three robot hinges connecting robot modules. The problem of optimal path planning assisting the proposed hRombo robot to clear optimally all predefined tiles within the arbitrary workspace is considered a classic Travel Salesman Problem (TSP), and this TSP is solved by the reinforcement learning (RL) approach. The RL's reward function and action space are based on robot kinematic and the required energies, including transformation, translation, and orientation actions, to move the robot inside the workspace. The CTPP for the hRombo robot is validated with conventional complete coverage methods in simulation and real workspace conditions. The results showed that the CTPP is suitable for producing Pareto plans that enable robots to navigate from source to target in different workspaces with the least consumed energy and time among considered methods.

Routine cleaning the pavement is an essential requirement to maintain a sustainable environment for social life. The different width and type of pavements raise the challenges for autonomous vehicles with fixed shape to operate effectively. In this paper, we introduce the vision based reconfiguration of self-reconfigurable pavement sweeping robot called Panthera, which can adjust its frame width to ease the cleaning tasks to become friendly with different pavement geometry. The expansion and compression operations of the Panthera width are implemented by rotating one high torque motor connecting with the lead screw rod to change the opening angle of linkage hinges. The Panthera cleaning and locomotion operations are synchronized with changing the robot width according to the output of detected pavement width. To this end, the segmented pavement leveraged on the masked based deep convolutional neural network (DCNN) is used as input for the proposed closed-loop feedback control method, enabling the robot to adjust the requirement of changing the width during locomotion accurately. The proposed PID scheme takes into account the robot kinematic design with the flexibility of width changing modes. The experiments were carried out in real environments demonstrated the autonomous reconfiguration robot width with various locomotion scenarios on pavements of varying width.

Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot’s ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site’s lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results.

Mobile service robots experience excessive vibrations when travelling over uneven surfaces in their workspace, increasing the degradation rate of the mechanical components or disrupting the robot’s sensing abilities for proper localization and navigational capabilities. Robot inclusivity principles can determine the suitability of a site for robot performance by considering the ground’s unevenness. This paper proposes a novel framework to autonomously evaluate the Robot Inclusivity Level of buildings based on surface unevenness (RIL-SU) by quantifying the surface unevenness of floor surfaces. The surface unevenness values are converted to RIL-SU using a rule-based approach, and the corresponding RIL-SU is tagged to the map location. A coloured heatmap based on the RIL-SU values is created as a visual representation of the RIL-SU of a given space. This heatmap would be useful for modifying the environment to make it more robot-friendly or restrict the robot’s operation in certain areas to avoid possible robot failures. The experimental results show that the proposed framework can successfully generate a valid RIL-SU heatmap for building environments.