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.

Size reconfigurable robots have been introduced for coverage applications to improve performance. The size reconfiguration ability allows a robot to access narrow areas in a smaller size while covering open spaces in a larger size, improving productivity. This paper proposes a novel CPP method consisting of an Overlapping Reduction Criterion (ORC) and a Reconfiguration Reduction Criterion (RRC) for a size-reconfigurable robot to improve performance in dynamic workspaces. A Glasius Bio-inspired Neural Network (GBNN) is adapted to guide the robot toward unvisited cells considering neural activity variation. The size variation is managed by utilizing a collection of grid maps generated for various size configurations of the robot. The RRC and ORC penalize the movements requiring size reconfigurations or creating isolated unvisited regions in the decision-making process of next movement selection yielding to reduce reconfigurations and overlapping. According to the results, the proposed CPP method surpasses state of the art in terms of performance indexes reconfiguration count, overlapping, path distance, and coverage time by significant margins.

While agricultural robotics has made great strides in recent years, manipulation of plants for tasks such as staking and harvesting remains highly challenging due to the high variability in dynamics and deformable nature of plants. To address the challenges created by dynamics uncertainty, we develop a system applying an occupancy-belief planning concept to plant manipulation for staking. We first train a dynamics model that predicts a per-pixel probability that the plant occupies the corresponding slice in space after a drag action using a large set of simulators. This model is then used to plan a manipulation action that maximizes the probability areas swept by the stake tying tool’s operating region are occupied by the plant, and minimize the probability areas swept by the non-operating side regions of the tool are occupied. We demonstrate our method both in simulation and with zero-shot sim-to-real transfer to a physical implementation. We show that adding consideration of belief through use of occupancy-belief allows our method to outperform both the visual foresight type approaches it is based on and other baselines and ablations, especially in the real-world case.

Efficient fire drill evacuation in high-rise buildings demands real-time perception and optimized path planning. This study presents an autonomous evacuation framework that integrates deep learning-based object detection with multi-objective trajectory planning. Using RGB-D vision, the system detects human traffic and staircases, projecting them into the robot’s workspace. The evacuation task is modeled as a Moving Target Traveling Salesman Problem (MT-TSP) and solved via a modified NSGA-II algorithm, balancing travel time, path length, safety, and accessibility. Experiments show over 90% detection accuracy and significant improvements in navigation efficiency, demonstrating the effectiveness of combining deep learning with evolutionary optimization for robotic fire drill planning.

In reconfigurable robotics, intra-reconfiguration enables a robot to change its functional abilities, while inter-reconfiguration manipulates the specification limits of the robot hardware. Although the versatility of inter-reconfigurable robots is desired in advanced autonomous systems, the O(n3) algorithm computational time complexity challenge comes when multiple modular robots combine and reconfigure into a bigger form structure for autonomous navigation tasks. This phenomenon has limited the inter-reconfiguration potential of expansion, versatility, and robustness. In this paper, a navigation framework with non-complex transformation states is proposed for inter-reconfigurable robots to perform combining and splitting control dimensions. Simulations have shown the complexity from O(n) to constant time O(1) in the reconfiguration states of the framework on a considerable number of robot agents. Additionally, a set of inter-reconfigurable robots, Wasp Biggie, was used to demonstrate the proof-of-concept in experiments as a fully functional centralized planner system. These experiments showed outperforming results on the consistent utility of CPU consumption while performing navigation and reconfiguration. Note to Practitioners—This study aims to provide controls for combining multiple robots into a single system. The research study enables the robots in the system to vary and manipulate their physical structure and mechanism limitations. Onboard computation resources are often finite and incapable of computing for multiple functions that are actively in demand. Hence, this paper is motivated by the severe increase in computational demands common in a multi-robot centralized system. The paper has provided the technical details of the system architecture of the state machine and how it integrates with a typical navigation stack. The state-machine of the framework can be easily constructed using the SMACH package in the Robot Operating System and integrated by reconstruction of the navigation stack by providing the command velocity as an input and producing the transformed command velocity for the controls of the respective robots. The outcome of the paper provides a method of control in inter-reconfigurability and takes in a consistent amount of computational resources as the number of robots varies in utilization.

Complete coverage path planning (CCPP) is a trending research area in floor cleaning robotics. CCPP is often approached as an optimization problem, typically solved by considering factors, such as power consumption and time as key objectives. In recent years, the safety of cleaning robots has become a major concern, which can critically limit the performance and lifetime of the robots. However, so far, optimizing safety has rarely been addressed in CCPP. Most of the path-planning algorithms in literature tend to identify and avoid the hazards detected by the robot’s perception. However, these systems can limit the area coverage of the robot or pose a risk of failing when the robot is near a hazard. Therefore, this article proposes a novel CCPP method with the awareness of risk levels for a robot to minimize possible hazards to the robot during a coverage task. The proposed CCPP strategy uses reinforcement learning (RL) to obtain a safety-ensured path plan that evaluates and when necessary, avoid the hazardous components in their environment in real time. Furthermore, the failure mode and effect analysis (FMEA) method has been adopted to classify the hazards identified in the environment of the robot and suitably modified to evaluate the risk levels. These risk levels are used in the reward architecture of the RL. Thus, the robot can cross the low-risk hazardous environments if it is necessary to obtain complete coverage. Experimental results showed a noticeable reduction in overall risk faced by a robot compared to the existing methods, while also effectively achieving complete coverage.

Floor cleaning robots have been developed to cater to building maintenance needs. Complete area coverage is crucial for a floor cleaning robot, and its morphology design plays a vital role in realizing complete area coverage. However, floor cleaning robots with fixed morphologies have difficulty in achieving a high area coverage performance. Mass customization of a robot’s morphology would improve its productivity in terms of area coverage. This paper proposes a novel system that can be used for mass customizing the morphology of a robot to improve area coverage performance in an environment of interest. The customized morphology is determined through an optimization technique by considering an environment of interest and design constraints. The area coverage of a candidate morphology design is evaluated by simulating the robot navigation in an environment of interest. Generalized pattern search, particle swarm optimization, and surrogate optimization are independently considered optimization techniques. Experiments have been conducted considering the cases of robot deployments. The statistical conclusions on experimental results validate that the proposed system can synthesize a morphology that significantly improves the area coverage performance in an environment of interest.

Resource sharing is a crucial part of a multi-robot system. We propose a Boolean satisfiability problem (SAT) based approach to resource sharing. Our key contributions are an algorithm for converting any constrained assignment to a weighted-SAT based optimization. We propose a theorem that allows optimal resource assignment problems to be solved via repeated application of a SAT solver. Additionally we show a way to encode continuous time ordering constraints using Conjunctive Normal Form (CNF). We benchmark our new algorithms and show that they can be used in an ad-hoc setting. We test our algorithms on a fleet of simulated and real world robots and show that the algorithms are able to handle real world situations. Our algorithms and test harnesses are open source and build on Open-RMF’s fleet management system.

Achieving complete area coverage in robotics is an essential aspect for applications such as cleaning and patrolling. While multi-agent frameworks have been implemented to address the challenge of complete coverage, the area coverage performances are hindered by physical constraints and dynamic obstacles that cause inaccessibility to certain areas of the environment. Reconfigurable robots have been adopted to mitigate this issue as the independent alteration of the morphologies during deployments enables overcoming tight spaces to access obstructed areas. Hence, this paper proposes a Multi-Agent Reinforcement Learning (MARL) framework leveraging the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to enable a team of reconfigurable robots to achieve complete coverage under the presence of dynamic obstacles. The framework is modelled to allow the robots to coordinate and plan their motions effectively while using shape adaptability to access narrow spaces while avoiding dynamic obstacles. Experimental results demonstrated the framework’s ability to be generalised even when scaled up to a different number of agents across larger environments.

Path planning is crucial in numerous robotic applications. Reconfigurable robots possess the capability to alter their shape, enabling access to confined spaces, a task challenging for fixed-shape robots. However, existing path planners for reconfigurable robots are typically designed with predefined motion patterns for reconfiguration, lacking adaptation to space availability and executing reconfiguration only when the robot is static. This reliance on predefined patterns limits the potential of reconfigurable robots to navigate through confined spaces. This paper proposes a novel path-planning approach based on dynamic reconfiguration to address this limitation. The proposed method employs two nested A* algorithms modified to handle reconfiguration and efficient search, termed Dynamic Reconfiguration integrated Nested A* (DRiNA*). Experimental results demonstrate the proposed method’s ability to find feasible paths for robot navigation using dynamic reconfigurations in confined spaces, surpassing the capabilities of existing path planners. The scalability of the proposed method to reconfigurable robots with varying numbers of blocks is also confirmed. Additionally, DRiNA* significantly reduces energy consumption compared to existing path planners.

Simultaneous Localization and Mapping (SLAM) technology is a rapidly developing field in robotics. Most existing SLAM algorithms lack robustness in dynamic environments because moving objects can influence mapping and localization accuracy, making it challenging for robots to identify moving objects and understand their surroundings. Though some works proposed semantic SLAM methods, they rely on point-based feature extraction and matching algorithms with semantic information and simply exclude dynamic objects. In this work, we proposed real-time RGB-D SLAM to combine point, line, and plane features with object detection to increase the robustness in dynamic environments. The proposed method combines object detection, feature points, and lines to identify moving objects and uses feature planes and semantic information to identify constrained moving objects. Thus, the localization accuracy can be improved under dynamic environments by excluding or using dynamic objects. Experiments were conducted on a public TUM dataset and in a real-world environment. The result shows that the proposed SLAM algorithm can increase the dynamic object detection speed and the robustness of SLAM performance compared to state-of-the-art in dynamic environments.

The proliferation of robotic applications has been notable in recent years, spanning a diverse range of use cases. Ensuring robot safety is crucial for safeguarding both robots and humans, as well as the infrastructure they operate within. Robots navigating indoor environments are particularly susceptible to hazards ranging from minor incidents to severe damage. Therefore, it is essential to consider potential risks and ensure safe navigation while maintaining efficiency. This paper proposes a path-planning method that prioritizes robot safety and path efficiency. Ant Colony Optimization (ACO) is employed in this method to find optimal solutions that balance path distance and the risks faced by robots. Experimental results validate that the proposed approach significantly reduces potential hazards robots may face in various environments during navigation compared to existing methods. Importantly, this risk reduction is achieved without compromising path distance or accessibility.

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.

Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot’s coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot.

Coverage Path Planning (CPP) is an essential feature of robots deployed for applications such as lawn mowing, cleaning, painting, and exploration. However, most of the state-of-the-art CPP methods are proposed for fixed-morphology robots, and the coverage performance is limited by physical constraints such as the inaccessibility of narrow spaces. Apart from area coverage, productivity depends on coverage time and energy usage. A robot capable of varying its footprint size could be a solution for improving productivity in these aspects. In addition to that, the environments, where robots are deployed for coverage, are often subjected to changes causing uncertainties. Therefore, this paper proposes an online CPP scheme for a size-variable robot to improve coverage productivity. The navigation planning of the proposed Size-Variable CPP (VSCPP) scheme has been implemented by adapting a Glasius bio-inspired neural network that guides a robot in an efficient path for coverage while coping with dynamic changes. The size variation required for a situation is determined by analyzing a set of occupancy grid maps corresponding to the size steps of the robot. According to the results, the proposed VSCPP can ascertain coverage while coping with dynamic changes in an environment. The reduction of the coverage time due to the size variability is significant compared to a robot with no VSCPP scheme.

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.

Tracking multiple faces online in unconstrained videos is a challenging problem as faces may appear drastically different over time and identities can be inferred only based on information available from past frames. Previous tracking methods focus on face information without reference to other modality information such as a person’s overall body appearance, leading to suboptimal performance. In this paper, we propose a new online multi-face tracking method, called online multi-face tracking with multi-modality cascaded matching (OMTMCM), to improve the tracking performance by using both face and body information. The proposed OMTMCM consists of two stages, namely detection alignment and detection association. In the first stage, a detection alignment module is designed to align face detection with body detection from the same person for the subsequent detection association. In the second stage, a cascaded matching module is designed to associate face detections across frames to locate trajectory of each target face by using both face and body information. Specifically, aligned face-body detections in the current frame are matched in a cascade manner with body and face features that are selected from past frames and stored in the designed feature memory. In this way, our method can track multiple faces online with both face and body information while eliminating the possibility of face detection and body detection from the same person being separately assigned with different identities. Experimental results demonstrate our method is on par with or better than other online tracking methods for multi-face tracking.

Area coverage is essential for robots used in cleaning, painting, and exploration applications. Reconfigurable robots have been introduced to solve the area coverage limitation of fixed-shape robots. The existing global coverage algorithms of reconfigurable robots are limited to consideration of a limited set of predefined shapes for the reconfiguration and do not consider the exact geometrical shape of obstacles. Therefore, degraded coverage performance could be observed from the existing methods. On the other hand, the coverage methods that consider reconfiguring beyond a limited set of predefined shapes are limited to local coverage. Furthermore, these methods only consider a single reconfiguration for the coverage. Therefore, this paper proposes a novel coverage method for a reconfigurable robot consisting of both global and local path planners. The global path planner uses boustrophedon motion combined with the A * algorithm. The optimum grid positioning that maximizes the global coverage is determined through a Genetic Algorithm (GA). The local coverage planner performs continuous reconfig-uration of the robot to adequately cover obstacle zones while navigating through narrow spaces without collisions. A GA is used to determine the reconfiguration parameters of the robot at each instance of the local coverage. Simulation results confirm that the proposed method is effective in performing both global and local coverage path planning for improving the area coverage performance.

In recent years, a rising trend of floor cleaning robots could be observed in the consumer electronic market. Area coverage performance is a crucial factor that determines the overall productivity of a floor cleaning robot. Nevertheless, the area coverage performance of commercially available floor cleaning robots is limited due to narrow spaces resulting from complex furniture arrangements. Traditionally, new robot designs (both hardware and algorithmic) are explored to over-come the coverage limitations. Developments of reconfiguration mechanisms and path planning algorithms for floor cleaning robots could be considered as examples. This paper proposes a novel concept called “design by robot,” enabling a floor cleaning robot to make suggestions on workspace modifications to maximize its area coverage performance in a given workspace. In this regard, the robot analyzes a workspace to be cleaned through internal simulations based on the metric map of the workspace. A metaheuristic optimization technique determines the optimum placings of objects. Particle Swarm Optimization (PSO), Surrogate Optimization (SO), and Generalized Pattern Search (GPS) are individually used in this regard. Experiments, including scenarios of robot deployments, have been considered for validation. The statistical outcomes of the experimental results validate that the area coverage performance of a floor cleaning robot could be significantly improved by considering the workspace modifications suggested by the robot. Moreover, the proposed concept “design by robot” enables users to gain significantly improved performance from floor cleaning robots through collaboration.

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.

Vacuum-adhesion-based climbing robots have been developed to cater to the demands in the cleaning and inspection work of airplanes. A robot intended to clean and inspect an airplane faces a Risk of Adhesion (RoA) based on the robot and the surface conditions, such as worn-out suction cups. These sorts of underlying conditions are not easily noticeable for an operator of a robot and might lead to catastrophic events. Therefore, the ability of a robot to self-assess the RoA in a scenario and notify the operator is crucial for ensuring safety. Particularly, an aircraft inspection robot should have adhesion awareness. This paper proposes a novel method to self-assess the RoA of a vacuum-adhesion-based robot intended to clean and inspect airplanes. The RoA is assessed by a fuzzy inference system that analyzes the present pressure difference and the current duty setting of the vacuum pump of a robot. The robot operator can collaborate with the robot to take precautions based on the assessed RoA to ensure safety. The outcomes of the experiment conducted on an airplane skin validate the ability of the proposed method to assess the RoA associated with heterogeneous operating conditions effectively. Thus, the utilization of the proposed method would improve the safety of a vacuum-adhesion-based robot intended to clean and inspect airplanes.

Estimating the microbial infestation profile of an area is essential for an effective cleaning process. However, current methods used to inspect the microbial infestation within a spatial region are manual and laborious. For large regions that require automated cleaning, conventional methods of microbial examination are not practical. We propose a novel robot-aided microbial density estimation and mapping framework using an in-house developed biosensor payload onboard a mobile robot. The biosensor estimates the degree of microbial infestation in Relative Light Units (RLU) using the natural bio-luminescence reaction. The global distribution of microbial infestation is approximated through the Radial Basis Function (RBF) and Nearest Neighbour (NN) interpolation algorithms. The proposed method is implemented on an in-house developed mobile robot called Beluga. The framework's validation and usefulness are demonstrated quantitatively through real-world experiment trials.

Cleaning robots are one of the market dominators in the commercialized robot space. So far, numerous robots have been introduced that can perform cleaning tasks in various settings, including floor, pavement, pool, lawn, windows, etc. However, none of the existing commercial cleaning robots targets the staircase, commonly found in multi-story buildings. Even though few works in the literature introduced robotic solutions for staircase cleaning, they primarily focused on cleaning the ascending staircase often, with a loose connection to access the descending staircase. In this paper, we propose a novel autonomous reconfigurable robotic platform called sTetro-D that can autonomously detect the descending staircase, approach the step, and perform area coverage in an unknown environment. The developed autonomy framework consists of two modes which are search mode and clean mode. In search mode, we implemented an RGB-D camera-based fusion technique wherein we combined the image bounding box from DCNN (Deep Convolution Neural Network) with the depth information to find the 3D first step pose that assists the robot in approaching it precisely. After the successful stair approach, the cleaning mode enables the staircase area coverage process. We described all these aspects and concluded with an experimental analysis of the proposed robotic system in a real-world scenario. The results demonstrate that the robot has a significant performance in detecting the descending staircase, staircase approach, and area coverage.

Area coverage is crucial for robotics applications such as cleaning, painting, exploration, and inspections. Hinged reconfigurable robots have been introduced for these application domains to improve the area coverage performance. However, the existing coverage algorithms of hinged reconfigurable robots require improvements in the aspects; consideration of beyond a limited set of reconfigurable shapes, coordinated reconfiguration and navigation, and online decision-making. Therefore, this paper proposes a novel online Complete Coverage Path Planning (CCPP) method for a hinged reconfigurable robot. The proposed CCPP method is designed with two sub-methods, the Global Coverage Path Planning (GCPP) and Local Coverage Path Planning (LCPP). The GCPP method has been implemented, adapting a Glasius Bio-inspired Neural Network (GBNN) that performs online path planning considering a fixed shape for the robot. Obstacle regions that the GCPP would not adequately cover due to access constraints are covered by the LCPP method that considers concurrent reconfiguration and navigation of the robot. A genetic algorithm determines the reconfiguration parameters that ascertain collision-free coverage and access of obstacle regions. Experimental results validate that the proposed online CCPP method is effective in ascertaining the complete area coverage in heterogeneous environments, including dynamic workspaces. Furthermore, the deployment of the LCPP method can considerably improve the coverage.

Area coverage is demanded from the robots utilized in application domains such as floor cleaning. Even though many advanced coverage algorithms have been developed, the area coverage performance is limited due to the inaccessibility of narrow spaces caused by physical constraints. Reconfigurable robots have been introduced to overcome this limitation where reconfigurability could help in assessing narrow spaces. Nevertheless, the state-of-the-art reconfigurable robots are not capable of changing the morphology size and shape as a single entity. Therefore, this paper proposes a novel design of a reconfigurable robot with a size-adaptive coverage strategy. The reconfiguration mechanism is designed in such a way that the robot can independently expand or shrink its size along the principal planar axes, where the behavior allows the change of size and shape. The coverage strategy is based on boustrophedon motion and the A* algorithm modified for accessing narrow areas using the size adaptability. The design of the robot is detailed in the paper, including electro-mechanical aspects, design considerations, and the coverage path planning method. Experiments have been conducted using a prototype of the proposed design to analyze and evaluate the characteristics and the performance of the robot. The results show that the proposed robot design can improve the productivity of a floor cleaning robot in terms of area coverage and coverage time.

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex environments is more challenging than conventional fixed-shaped robots due to their flexible degrees of freedom while navigating through tight spaces. This article presents a novel algorithm, energy-efficient batch informed trees* (BIT*) for reconfigurable robots, which incorporates BIT*, an informed, anytime sampling-based planner, with the energy-based objectives that consider the energy cost for robot’s each reconfigurable action. Moreover, it proposes to improve the direct sampling technique of informed RRT* by defining an$L^2$greedy informed setthat shrinks as a function of the state with the maximum admissible estimated cost instead of shrinking as a function of the current solution, thereby improving the convergence rate of the algorithm. Experiments were conducted on a tetromino hinged-based reconfigurable robot as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces energy-efficient solution paths, and outperforms existing techniques on simulated and real-world experiments.

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.

Being as a robotic companion is an extensive application of on-body robots; yet, as an emerging type of robots, few previous works focus on the design of on-body companion robots from the users' perspective, remaining users' expectations towards this type of robots unclear. To assist designers in the design process of on-body companion robots, we surveyed users' expectations towards on-body companion robots (n=215) by a questionnaire constituting of questions on factors that may affect robot acceptance, including robot functionality, robot appearance, and robot social ability. Based on the survey results, we stated design guidelines for the design of on-body companion robots supporting designers with insights into users. To demonstrate how to design on-body companion robots based on our findings, we organized a workshop with experienced designers to develop a conceptual on-body companion robot, and they proposed Bubo, an example prototype of on-body companion robot.

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.

Cleaning requirements of glass façades of high-rise buildings have been significantly increased in recent years due to the growth of the construction industry. The conventional cleaning methods for high-rise buildings require human labor where efficiency, cost, and safety are major concerns. Therefore, robotic systems that can climb and clean glass fagades in high-rise buildings have been developed. Capability to attach to a façade surface is one of the critical requirements of a glass façade cleaning robot. Diverse approaches have been proposed for adhesion of cleaning robots to a glass façade. Active vacuum suction mechanisms are widely used for these robots since they provide better controllability to move the robots smartly on the surface. Notably, those are decent for a reconfigurable robot that can transit between window frames. The suction mechanism of a glass façade cleaning robot must provide a reliable suction force to make the robot stay safely and move smartly on a façade. Nevertheless, these suction mechanisms can be failed due to improper fastening between a façade and the mechanism, which may lead to safety and operational issues. Therefore, this paper proposes a novel method to realize the adhesion-awareness of a glass façade cleaning robot. The adhesion-awareness is realized by analyzing The current drawn by the motors attached to the impellers of the vacuum mechanisms. Experiment results validate the capability of the proposed approach in raising the adhesion-awareness of a façade cleaning robot.

Research focuses on robots related to area coverage applications such as cleaning, painting, demining, lawn moving, and inspection is gaining significant momentum in recent years. The majority of such research platforms faces significant performance issues while accessing narrow and constrained spaces due to their fixed morphology. To this end, we have developed a novel self-reconfigurable robot platform named as “hTetran” inspired from Polyabolo which can change its morphology to any of the six tetrabolo shapes with an objective of maximizing the area coverage. This paper presents the system design, reconfiguration theory, and locomotion modules of the developed robot, including the adaptation of Polyabolo tiling theory as a coverage path planning strategy for autonomous navigation. The paper concludes with a set of experiments in a mocked office room setup that validates the efficiency of the proposed robot in terms of area coverage. Our experimental trials indicate that the “hTetran” brings out a higher area coverage performance in all considered experimental cases.

This paper proposes an improved controller with a friction estimator for the path tracking of a cleaning robot having a scrubbing brush, motivated by the presence of the positive influence of brush friction on robot propulsion. For the controller and the estimator, the dynamics of a cleaning robot with a scrubbing brush is represented by a model incorporating the LuGre dynamic friction model. This dynamic robot model is transformed into appropriate forms in controller and estimator design. The controller employs a sliding mode control (SMC) law improved to exploit the friction for achieving a control target. The estimator provides estimates of the state and parameters of the dynamic model in an unscented Kalman filter (UKF) framework to calculate the friction force. The performance of the proposed controller with the estimator is tested through numerical simulations. The simulation results illustrate that the proposed approach is effective for the path tracking of cleaning robots with less input torque.

Service robots that possess social interactive capabilities are vital to cater to the demand in emerging domains of robotic applications. A service robot frequently needs to interact with users when performing service tasks. The comfortability of users depends on the human-robot proxemics during these interactions. Hence, a service robot should be capable of maintaining proper proxemics that improves the comfort of users. The proxemics preferences of users might depend on diverse attributes of a robot, such as emotional state, noise level, and physical appearance. Therefore, it is vital to gain a better understanding of a robot's attributes which influence human-robot proxemics behavior. This paper contributes to an exploratory study to analyze the effects on human-robot proxemics preferences due to a robot's attributes; facial and vocal emotions, level of internal noises, and the physical appearance. Four sub-studies have been conducted to gather the required human-robot proxemics data. The gathered data have been analyzed through statistical tests. The test statistics reveal that facial and vocal emotions, internal noise level, and the physical appearance of a robot have significant effects on proxemics preferences of humans. The outcomes of this exploratory study would be useful in designing and developing human-robot proxemics strategies of a service robot that would enhance social interaction.

This research work presents the development of a novel tetris inspired reconfigurable floor cleaning robot - hTetro, utilizing the hinged dissection theory of polyominoes. The developed robot platform is capable of transforming between any of the seven set of one-sided tetromino morphologies according to the perceived environment with an objective of maximizing the coverage area. Experiments were performed across two different settings to systematically compare the coverage area performance of the developed hTetro robot and a commercially available fixed morphology robot platform. Results indicate significantly higher coverage area performance in the case of hTetro due to its ability to assume optimal morphology in relation to navigating environment.

The use of biologically inspired robots in therapeutic settings could offer new possibilities for improving learning and social interaction abilities of children with autism. This paper introduces a novel teaching method, Adapted Model-Rival Method, together with a parrot-inspired robot (KiliRo) to help children with autism in learning and social interaction. The proposed indirect teaching method and the parrot-like robot morphology were tested with 9 children identified with autism. The test was conducted for 5 consecutive days for the same participants at the same place. In this study, the emotions of participating children during the experiment were analyzed using an automated emotion recognition and classification system, Oxford emotion API through facial images. Totally, 580 pictures were taken to obtain 2360 individual facial emotion values for evaluation. The results show that the happiness of subjects improved from day 1 through day 5 of the study through interacting with the robot. It is also reported that the participating children were attracted to the robot when it was exhibiting its learning abilities. Our study also indicates that the children with autism are not afraid of parrot-like robots and are happy to interact with it.

Ergonomics is the study of designing more human-friendly products, systems or processes for human. By extending this concept to robotics field, we propose robot ergonomics which is a transdisciplinary approach that brings together roboticists, product designers, and architects to solve numerous unsettled research problems or technology bottlenecks in robotics community through designing products for robots. This paper focuses on a case study of chair design for Roomba. Seven design criteria are proposed and intensive experiments are performed to validate the criteria using 22 chairs. Based on such empirical design strategy, three generic principles (i.e., observability, accessibility, and safety) of chair design are extracted for Roomba.

The long-term goal of this study is to realize a locomotion control for snake-like robots on various environments. A key point for the locomotion control is the normal direction friction force of each link of the robot. Because, a real snake can locomote by utilizing the difference of frictions in the propulsive and in the normal direction. In previous our study, a locomotion control of the snake-like robot considering side-slipping has been proposed. However, there are some problems to realize the control by a real machine. As the problems, all state of the snake-like robot is supposed to be observed, and computational load of the control law is too large to be real-time. To solve these problems, this paper aims to develop a state estimation of the snake-like robot in consideration of side-slipping by utilizing an observer. Moreover, a rotational elastic actuator consisting of a motor and a spring is installed for the robot to adapt to various environments. Motion equations of the snake-like robot with rotational elastic actuators and side-slipping effect are derived. Constraint forces of the normal direction of each link are also formulated in the modeling process. A type-I observer based on State Dependent Riccati equation: SDRE is utilized to detect a slipping link of the robot. This detection is based on the generalized coordinate of the robot link estimated by the observer. Numerical simulations are given to verify the effectiveness of the proposed method.

In particular, service robotics is strongly tided to highly accurate navigational tasks where pathway tracking is a practice commonly carried out through control algorithms. This study proposes theoretical non-linear pathways presented as kth-degree polynomials as model references. Our proposal establishes a proportional control with a variable model reference at the level of second order derivatives to which the robot's motion is adapted on-line. The robot's fixed frame Cartesian trajectory model is inversely transformed to wheels' angular acceleration components, which are the reference models. Although, our proposal may be applied to any type of wheeled robot's kinematic structure, we are presenting an example of two active wheels with differential velocity modality. Obtained results raised from successful experimental practice and numerical simulations as well, which depict the capability of the proposed model control.

One of the key benefits of reconfigurable legged robots is their ability to move in a non-continuous way enabling it to provide better mobility over rough and irregular terrains. Legged robots should maintain it stability during rest and motion with minimum number of legs while maintaining its functionality during various gait generation and its resultant application. In this paper we have presented a stability analysis for reconfigurable Klann mechanism based quadruped by changing leg arrangement for six useful gait cycles. The platform stability based on COG (Center of Gravity) for this six useful gait cycles are validated through simulated results.

Staircase detection in an important ability required by indoor robots, allowing for multi-floor exploration in 3D environments. We present an algorithm for stair-case detection from point-cloud data based on a new minimal 3D map representation and the estimation of step-like features that are grouped based on adjacency in order to emerge dominant staircase structures. Experiments performed using noisy RGB-D sensor data from robot exploration trials showed a reliable detection performance under varying conditions.

Research focus on service robots that deals with applications related to healthcare, logistics, residential, search and rescue are gaining significant momentum in the recent years. Their social and economic relevance is more than evident. Yet, while much has been researched about “designing robots” focusing on sensing, actuation, mobility and control of service robots, little work has been done on “design for robots” that looks at designing preferred artefacts or environments for such robots. In this work, we propose a new philosophy of robot inclusive spaces, a cross disciplinary approach that brings together roboticians, architects and designers to solve numerous unsettled research problems in robotics community through design of inclusive interior spaces for robots where the latter live and operate. With a residential floor cleaning robot as a case study, we inductively derived a set of four design principles namely observability, accessibility, activity and safety that guides the realization of an inclusive space for these service robots. Also, the suggested principles are further defined, analysed and validated for their merits in this paper.

Legged robots are able to move across irregular terrains and some can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This paper reports a reconfigurable design approach to robotic legged locomotion that produces a wide variety of gait cycles, opening new possibilities for innovative applications. In this paper, we present a distance-based formulation and its application to solve the position analysis problem of a standard Theo Jansen mechanism. By changing the configuration of a linkage, our objective in this study is to identify novel gait patterns of interest for a walking platform. The exemplary gait variations presented in this work demonstrate the feasibility of our approach, and considerably extend the capabilities of the original design to not only produce novel cum useful gait patterns but also to realize behaviors beyond locomotion.

Theo Jansen mechanism is gaining attention among legged robotics researchers due to its scalable design, energy efficiency, low payload to machine load ratio, bio-inspired locomotion, and deterministic foot trajectory. In this paper, we present a novel position control strategy for Jansen walking robot derived using projection method for which energy based control forms the core. Numerical simulations are done to validate the efficacy of the designed controller. This work identifies an appropriate controller gain that decreases the overshoot necessary for successful real world deployments.

In this paper, we validate the extended neglect tolerance model for estimation of human robot team performance in relation to robot autonomy and compare its results with the traditionally adopted neglect tolerance model which assumes zero false alarms in human robot interactions. Extended neglect tolerance model estimates robot performance in human robot teams, where the human operator switches control between robots sequentially, based on acceptable performance levels, taking into account any false alarms and their respective demands. Experiments were performed with Robo-Erectus@Home, a service robot across tele-operation, and semi-autonomous modes of autonomy where a human operator controlled the robot to perform a walking assistant task. Measured false alarm demands and robot performances were largely consistent with the extended neglect tolerance model predictions for both autonomy modes. We also compared traditionally adopted neglect tolerance and extended neglect tolerance model for the same experimental design. The results showed that the latter offers better estimates of robot performance and attention demands, due to the inclusion of false alarms into the model.

This paper focuses on the creation of a realistic simulation of a humanoid robot in a virtual environment. This model will help researchers to implement and study different behaviours of the humanoid robot Robo-Erectus without its physical presence. The dynamics and the appearance of the robot and the other objects within the simulator are faithfully reproduced in the virtual environment. Moreover, the virtual robot can be controlled with the same program controlling the real robot. The simulator has been used successfully to generate and test behaviours for the robot to prepare for the RoboCup 2008.

As rehabilitation robots are increasingly serving to improve the quality of life for physically disabled people in clinical environments, the concept of emotional expressiveness in robots becomes increasingly important. The human perception of robot's emotional expressions plays a crucial role in human robot interaction. Virtual expression systems outperform hardware systems in realizing human like expressions due the limitations in hardware actuators and advances in animation tools. This paper evaluates the human perception of robot's emotional expressions with two different virtual emotional expression systems: one where the robot exhibits emotions through icon faced expressions and in other the robot exhibits emotions through human faced expressions in clinical environment. A rehabilitation HRI research robot Robbie is introduced and the results of the comparative study on human perception of robotpsilas emotional expressions with two different systems are discussed.

During last years, robotic research has explored Human-Robot interactions especially for the challenge of the robot companion. This type of robot would be equipped with perception, motion, and manipulation among other skills, to be help humans in daily tasks. Guidance is one of the tasks that the robot companion must perform. This paper describes Robbie, a wheeled robot companion uses for guiding missions. The Robbie combines visual clues, voice, distance and PC input to detect humans and features in the environment. To complete its task Robbie could receive extra information from smart beacons located in the building. The paper also explores two modalities to the guiding missions. Finally, experiments are presented in order to highlight the relevance of the approach.

In the field of human-computer interaction, natural goals, operators, methods, and selection rules language (NGOMSL) model is one of the most popular method for modelling knowledge and cognitive processes for rapid usability evaluation. NGOMSL model is a description of the knowledge that a user must possess to operate the system represented as elementary actions for effective usability evaluations. In the last few years, mobile robots have been exhibiting a stronger presence in the commercial markets and very little work has been done with NGOMSL modelling for usability evaluations in human-robot interaction discipline. This paper focuses on applying NGOMSL modelling for usability evaluation of human-humanoid robot interaction in soccer domain. Usability evaluations were performed and adequate results were obtained. Evaluated interaction design was adopted by our Robo-Erectus Junior version of humanoid robots in the RoboCup 2007 humanoid soccer league.