Complete coverage path planning (CCPP) is essential for autonomous cleaning robots, particularly in complex and variable environments where traditional, fixed-footprint designs may fall short. This paper presents adaptive Glasius bio-inspired neural network (aGBNN) approach to CCPP, specifically tailored for an omnidirectional self-reconfigurable cleaning robot (OSCR). The aGBNN method dynamically generates a complete coverage path by leveraging the ability to change sweeping footprint of the robot (SFR) assisted by reconfiguring brushes design. The sweeping is carried out both longitudinally and laterally, thereby complementing the omnidirectional locomotion with cleaning. Unlike conventional CCPP algorithms that assume a fixed robot footprint, the proposed aGBNN adapts in real-time to spatial and moving obstacles, significantly enhancing coverage efficiency. Experimental and simulation results demonstrate the advantage of the aGBNN approach, in terms of path length, and total time to complete area coverage compared to state-of-the-art methods.

Reconfigurable robots provide an attractive option for cleaning tasks, thanks to their better area coverage and adaptability to changing environment. However, the ability to change morphology creates drastic changes in the reconfigurable robot dynamics, and existing control design techniques do not take this into account. Neglecting configuration changes can lead to performance degradation and, in the worst scenarios, instability. This paper proposes to embed the changes arising from reconfiguration in the control design, via a switched uncertain Euler-Lagrangian model. Accordingly, a novel switched adaptive design is proposed for trajectory tracking. Closed-loop stability is assured using the multiple Lyapunov function framework, and the design is implemented and validated on a self-reconfigurable pavement cleaning mobile robot (PANTHERA). Note to Practitioners—Self-reconfigurable mobile cleaning robots, which can change their configurations as per the application requirements, are now predominantly used for cleaning and maintenance operations because of their better area coverage, less manpower requirement and consistent performance. However, the state-of-the-art control strategies for conventional robots cannot always ensure stability and performance under the simultaneous effects of configuration changes and uncertainties. The switched Euler-Lagrange model formulated in this work can capture the configuration changes of the robot and the proposed switched adaptive controller can tackle uncertainties of each configurations of the robot. The simulation and experimental results clearly show the potential issues of the state-of-the-art methods and the remarkable benefits of the proposed approach.

Autonomous mobile robots (AMRs) face challenges in efficiently covering complex environments. To navigate narrow and expansive areas, AMRs must have two essential attributes: compact size for confined spaces and larger size with omnidirectional locomotion for broader spaces. This study utilizes omnidirectional expand and collapse robots (OECRs) to demonstrate efficient area coverage. OECRs can collapse to navigate through confined spaces and expand for efficient coverage in broad spaces. However, current complete coverage path planning (CCPP) methods do not account for the expanded and collapsed states of OECRs. To address this, a depth-first search (DFS) approach is proposed for OECRs' CCPP, which can adjust the robotic footprint along the CCPP path to reduce path length. The proposed DFS outperforms the state-of-the-art CCPP in terms of increased area coverage and reduced distance traveled on a selected map.

Pavement cleaning is a labor-intensive, repetitive task and can be automated. Several autonomous pavement cleaning robots have been developed, pushing research towards their design and autonomous capabilities. Advances in design have been reported in earlier works on a self-reconfigurable robot with four independent steering drive (4ISD) capabilities, Panthera, for pavement cleaning and maintenance. Moreover, autonomous navigation requires sharp turns, heading angle adjustments, sideways movement, and locomotion without col-lision through constrained pavement conditions. The present work proposes an algorithm to ingeniously select the instan-taneous center of rotation (ICR) within the self-reconfigurable robot footprint and perform static rotation to adjust its heading angle during the waypoint navigation while avoiding collision with the constrained environment. Finally, the proposed algorithm is implemented, and experiments are conducted in real-world pavement scenarios. The experimental outcome success-fully demonstrates the self-reconfigurable robot's capability to navigate constrained pavement scenarios using the proposed algorithm during autonomous cleaning and maintenance tasks.

Autonomous vehicles are designed to elevate the efficiency of assigned tasks and ensure the safety of the environment in which they operate. This paper presents a research study focused on shared autonomy using a multi-layer fuzzy logic framework to build a relationship between an autonomous self-reconfigurable robot and a human user by switching control to the teleoperator to assist the robot when it faces challenging scenarios while keeping a good performance and maintaining a safe environment. A novel multi-layer fuzzy logic decision process with shared autonomy for a safety framework is proposed. It evaluates safety based on the robot's multi-sensor inputs, the teleoperator's attention level, and the configuration state of the self-reconfigurable robot and switches the operation mode, robot speed gain, and configuration state for performance and safety without compromises. The experimental outcome successfully demonstrates the self-reconfigurable robot's capability to navigate safely using shared autonomy in real-world pavement scenarios using the proposed algorithm during autonomous navigation.

An efficient sensors fusion framework in an autonomous robot is necessary for various functions like object detection and perception enhancement. Multi-sensor calibration techniques are used to fuse multiple static sensors into a single frame of reference. However, for reconfigurable robots, sensors can change pose during reconfiguration need a robust adaptive sensor fusion to account for the relative change in sensor position and orientation. We propose an adaptive sensor fusion framework that can be implemented on any reconfiguration robot to adjust calibration parameters. Our paper formulated an adaptive sensor fusion method, implemented it in real-time on an autonomous reconfigurable pavement sweeping robot called Panthera, and demonstrated qualitatively the accuracy of the proposed sensor fusion framework for environment perception during robot reconfiguring on the pavement.