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.

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.

The focus of this paper is (i) to derive a suitable dynamic model for a self-reconfigurable pavement sweeping robot PANTHERA and (ii) to design a robust controller for the same to tackle uncertainties stemming from the reconfiguration process, external disturbances and from actuator saturation. To meet the first objective, an Euler-Lagrangian dynamic model is proposed to incorporate the effects of configuration changes on the system dynamics. Based on this model, the second objective is met via designing a singular perturbation based robust controller which can tackle the aforementioned uncertainties without violating the actuation limits. To circumvent the vulnerability toward actuator saturation, the proposed controller is built on contraction theory, which, compared to a conventional Lyapunov theory based design, allows to improve closed-loop tracking performance without reducing the singular perturbation parameter. Experimental results on the PANTHERA reconfigurable robot validate the effectiveness of the proposed controller over the state of the art.

The holonomic and omnidirectional capabilities imparted to the mobile base platform depends mainly on two factors, i.e., the wheel design and its various arrangements in the platform chassis. This paper reports on the development of a novel omnidirectional spherical sectioned wheel named Ospheel. It is modular, and the spherical sectioned geometry of the wheel is driven using two actuators placed inside the housing above the wheel that rotates it independently about two perpendicular axes. The mechanical drive system for Ospheel consists of two gear trains, namely, internal spur gear and crown gear spatially assembled in orthogonal planes and are driven by two driving pinions. The kinematics of a single Ospheel is described, followed by the kinematic equation of a robot equipped with two Ospheels. Forward and inverse kinematic equations are derived explicitly. Experiments were carried out with the two Ospheels at a fixed inclination assembled with the base to illustrate the holonomic motion. The robustness of the wheel design is experimented with different trajectories and on different terrains.

The pavement cleaning is essential to maintain urban hygiene and keep the long stretch of pavements spick and span. This paper reports on the development of novel reconfigurable pavement cleaning robot named Panthera. Reconfiguration in Panthera is gained by the expansion and contraction of the body frame using a single lead screw shaft and linkages mechanism. It gives the capability to reshape itself based on factors like pavement width and pedestrian density. The independent steering action is derived using two in-wheels motors for each steering axis. This imparts the flexibility in motion and make system omnidirectional and allows the convenient movement of the robot in any direction along the pavement. It is powered using onboard batteries that generate lesser noise compared to the existing solution powered with gasoline. The modeling and steering kinematics is presented along with experimental results of the path followed and discussion supporting the robot's capability.

Active force control of an industrial robot with an end-effector force/torque sensor effectively handles compliant industrial tasks like assembly operations, surface finishing jobs, cooperative manipulation, etc. However, the robot still remains intrinsically unsafe for dynamically changing environment where the chances of the links coming in contact with the environment exists. This paper proposes a scheme for active force control at the end-effector using a six-component force/torque sensor through external force-control loop along with steady-state error compensator. In parallel, passive joint compliance was achieved by limiting the currents to joint motors based on an identified model of the robot under study. The proposed method was implemented on a KUKA KR5 ARC industrial robot and tested for passive compliance by colliding with another moving robot in its workspace. Active force control was tested to maintain a desired force on contact to demonstrate the effectiveness of the controller.