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.

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.

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 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.

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.

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.

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.

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.

Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, proxemics planner of a service robot should be cable of deciding the approaching proxemics based on user behavior. This paper proposes a method to decide the approaching proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.

Service robots have been developed to enhance the living standard of people. Users of these service robots are not experts in robotic domain. Hence, they prefer to have human-friendly features in these service robots. These service robots often need to navigate toward their users when accomplishing service tasks demanded by the users. Therefore, the ability of a service robot to approach a user in a human-friendly manner would increase the rapport between the user and the robot. This paper proposes a method for human-friendly approaching of a service robot toward a user who is having a conversation with another person. The algorithm behind the proposed approaching method has been designed in such a way that the robot can replicate the natural human tendencies to a greater extent. Natural approaching behavior of humans identified from a previously conducted human study has been utilized for this purpose. The proposed approaching method has been implemented on MIRob platform and a user study has been conducted to validate the capabilities of the proposed concept. The experimental results validate that the proposed approaching method of the robot is capable of maintaining the satisfaction of the users during approaches.

Development of intelligent service robots is a growing research area within the field of robotics. Users of these service robots expect human-like interaction abilities from the robots. These service robots must be capable of navigating in human populated environments by considering the comfort of the people for having smooth interaction with the users since many of the daily routine tasks involve the navigation. Therefore, a robot's way of approaching to users during interactions must be human-friendly in order to enhance the rapport between the human users and the robot. However, the natural approaching behavior of humans has to be studied in order to implement such human-like navigation abilities in service robots. Therefore, this paper contributes in a human study conducted for identifying the approaching behavior of a third person toward two persons who are having a conversation. The study has been conducted as three sub studies to identify the parameters that alter the approaching behavior. The key aspects of the approaching behavior of humans have been identified by analyzing the experimental scenarios. The capabilities required for a service robot for implementing a human-like approaching mechanism are discussed based on the outcomes of the human study. Furthermore, the directions for future investigation of human approaching behavior through human studies are also provided.