Complete Area Coverage Research Articles

Complete area-coverage path planner for surface cleaning in hospital settings using mobile dual-arm robot and GBNN with heuristics

Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities.

Growth of AlN coating on Al-6061 alloy surface

The objective of the study was to grow uniform nitride coatings over Al-6061 alloys to resist fluorine diffusion. This paper discusses a process of carrying out surface nitridation of an Al-6061 alloy substrate. Through this method, a dense aluminum nitride coating can be grown on Al-6061 alloy with complete area coverage of the samples having complex geometries. The method uses nitrogen gas alone as the nitrogen carrier, and not ammonia derivatives used in conventional processes. Magnesium powder was also used in the process to reduce the protective alumina layer on the alloy and to facilitate the aluminum nitride growth. The process led to uniform coating coverage over the flat sample surfaces as well as over the surfaces of drilled holes in the sample. The coatings were stable when subjected to several 400 °C thermal cycles.

Magneto-Mechanical Bilayer Metamaterial with Global Area-Preserving Density Tunability for Acoustic Wave Regulation.

2D metamaterials have immense potential in acoustics, optics, and electromagnetic applications due to their unique properties and ability to conform to curved substrates. Active metamaterials have attracted significant research attention because of their on-demand tunable properties and performances through shape reconfigurations. 2D active metamaterials often achieve active properties through internal structural deformations, which lead to changes in overall dimensions. This demands corresponding alterations of the conforming substrate, or the metamaterial fails to provide complete area coverage, which can be a significant limitation for their practical applications. To date, achieving area-preserving active 2D metamaterials with distinct shape reconfigurations remains a prominent challenge. In this paper, magneto-mechanical bilayer metamaterials are presented that demonstrate area density tunability with area-preserving capability. The bilayer metamaterials consist of two arrays of magnetic soft materials with distinct magnetization distributions. Under a magnetic field, each layer behaves differently, which allows the metamaterial to reconfigure its shape into multiple modes and to significantly tune its area density without changing its overall dimensions. The area-preserving multimodal shape reconfigurations are further exploited as active acoustic wave regulators to tune bandgaps and wave propagations. The bilayer approach thus provides a new concept for the design of area-preserving active metamaterials for broader applications.

Experimental Investigation for the Phase Change Material Barrier Area Effect on the Thermal Runaway Propagation Prevention of Cell-to-Pack Batteries

Thermal runaway propagation (TRP) is a primary safety issue in lithium-ion battery (LIB) applications, and the use of a thermal barrier is considered to be a promising solution for TRP prevention. However, the operating conditions of the battery are extremely complicated, such as fast charging, low-temperature heating and thermal runaway. To date, there is no consistent answer as to how to choose the appropriate thermal barrier for such a complicated working environment. In this study, the characteristics of hydrogel based on sodium polyacrylate are explored, and the impact of thermal barrier area on TRP is investigated through experiments. Due to the prismatic battery structure, thermal barriers placed between cells are designed with different areas (148 × 98 mm, 128 × 88 mm, and 108 × 78 mm). The results indicate that test 1 without a placed thermal barrier quickly completes the TRP process, and the thermal runaway (TR) behavior is more violent. With a thermal barrier that does not have full area coverage placed between cells (test 2 and test 3), the propagation time is prolonged, but TRP still occurs. Compared with test 1, the triggered temperature of T2 F (the front surface of cell 2) is reduced by 207.6 °C and 295.2 °C, respectively. The complete area coverage thermal barrier successfully prevents TRP, and the T2 F of cell 2 only reaches 145.4 °C under the phase change by the hydrogel. This study may suggest a safety design for battery modules and prevent propagation among batteries.

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage.

This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency.

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot.

One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Complete area coverage is a crucial factor for a floor cleaning robot. Self-reconfigurable tiling robots have been introduced over robots with a fixed shape for floor cleaning since they improve the area coverage by the flexibility of shape-shifting in cluttered environments. The existing coverage methods of reconfigurable tiling robots follow the tiling theory to cope with the area coverage problem. However, these methods merely consider a limited set of predefined shapes for the reconfiguration of a robot. The consideration of a limited set of predefined shapes for the reconfiguration impedes the ability of coverage to a certain extent in typical floor environments. Therefore, this paper proposes a novel method to improve area coverage of a tiling robot by reconfiguring according to the shape of obstacles. To this end, the required hinge angles for reconfiguring per the shape of an obstacle are determined by a genetic algorithm. The proposed method considers an optimized shape for reconfiguration in lieu of a limited set of predefined shapes. The coverage improvement of the proposed concept has been compared against the existing coverage methods of tiling robots to validate the performance. According to the experimental results, the proposed method surpasses the existing coverage methods of tiling robots from the perspective of area coverage, and the improvement is significant and noteworthy.

Complete Area Coverage Path-Planning with Arbitrary Shape Obstacles

Complete Area Coverage Path-Planning with Arbitrary Shape Obstacles

Multi-Agent Collaborative Path Planning Based on Staying Alive Policy

Modern mobile robots tend to be used in numerous exploration and search and rescue applications. Essentially they are coordinated by human operators and collaborate with inspection or rescue teams. Over the time, robots became more advanced and capable for various autonomous collaborative scenarios. Recent advances in the field of collaborative exploration and coverage provide different approaches to solve this objective. Thus scope of this article is to present a novel collaborative approach for multi-agent coordination in exploration and coverage of unknown complex indoor environments. Fundamentally, the task of collaborative exploration can be divided into two core components. The principal one is a sensor based exploration scheme that aims to guarantee complete area exploration and coverage. The second core component proposed is a staying alive policy that takes under consideration the battery charge level limitation of the agents. From this perspective the path planner assigns feasible tasks to each of the agents, including the capability of providing reachable, collision free paths. The overall efficacy of the proposed approach was extensively evaluated by multiple simulation results in a complex unknown environments.

Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot.

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.

Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot

Tiling robotics have been deployed in autonomous complete area coverage tasks such as floor cleaning, building inspection, and maintenance, surface painting. One class of tiling robotics, polyomino-based reconfigurable robots, overcome the limitation of fixed-form robots in achieving high-efficiency area coverage by adopting different morphologies to suit the needs of the current environment. Since the reconfigurable actions of these robots are produced by real-time intelligent decisions during operations, an optimal path planning algorithm is paramount to maximize the area coverage while minimizing the energy consumed by these robots. This paper proposes a complete coverage path planning (CCPP) model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost. To this end, a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) layers is trained using Actor Critic Experience Replay (ACER) reinforcement learning algorithm. The results are compared with existing approaches which are based on the traditional tiling theory model, including zigzag, spiral, and greedy search schemes. The model is also compared with the Travelling salesman problem (TSP) based Genetic Algorithm (GA) and Ant Colony Optimization (ACO) schemes. The proposed scheme generates a path with lower cost while also requiring lesser time to generate it. The model is also highly robust and can generate a path in any pretrained arbitrary environments.

Energy Consumption Estimation Model for Complete Coverage of a Tetromino Inspired Reconfigurable Surface Tiling Robot

As autonomous tiling devices begin to perform floor cleaning, agriculture harvesting, surface painting tasks, with minimal or no human intervention, a new challenge arises: these devices also need to be energy efficient and be constantly aware of the energy expenditure during deployments. Typical approaches to this end are often limited to fixed morphology robots with little or no consideration for reconfiguring class of robots. The main contribution of the paper is an energy estimation scheme that allows estimating the energy consumption when a tetromino inspired reconfigurable floor tiling robot, hTetro moves from one configuration to another for completing the area covering task. To this end, the proposed model applying the Newton-Raphson algorithm in combination with Pulse width modulation (PWM)-H bridge to characterize the energy cost associated with locomotion gaits across all valid morphologies and identify optimal area coverage strategy among available options is presented. We validate our proposed approach using an 8’ × 8’ square testbed where there exist 12 possible solutions for complete area coverage however with varying levels of energy cost. Then, we implemented our approach to our hTetro platform and conducted experiments in a real-life environment. Experimental results demonstrate the application of our model in identifying the optimal area coverage strategy that has the least associated energy cost.

GRID BASED APPROACH FOR EFFICIENT ROUTING IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) supports wide range of applications such as health and environment monitoring, imaging, tracking, and biomedical research. Hence they can be useful in a number of situations. Wireless sensor networks sense some physical scenario and propagate the sensed data to the base station with the help of intermediate nodes. Area coverage is an important issue in these networks. The objective of complete area coverage is to find out redundant nodes and deactivate them, so that the remaining active nodes can cover the deployed area. Among the various existing approaches for area coverage in WSN, a grid based approach has been proposed that provides a better way to select the active nodes or deactivate the redundant nodes from the grid rather than choosing the nodes from the deployed area. The existing approach goes with certain limitations such as how many numbers of grids need to be made in a certain deployed area with a certain number of nodes and how much percentage of nodes need to be selected from each grid. Our proposed protocol outperforms the existing grid based protocol with respect to energy saving, network lifetime and End to End delay.

LakeTime: Automated Seasonal Scene Selection for Global Lake Mapping Using Landsat ETM+ and OLI

The Landsat series of satellites provide a nearly continuous, high resolution data record of the Earth surface from the early 1970s through to the present. The public release of the entire Landsat archive, free of charge, along with modern computing capacity, has enabled Earth monitoring at the global scale with high spatial resolution. With the large data volume and seasonality varying across the globe, image selection is a particularly important challenge for regional and global multitemporal studies to remove the interference of seasonality from long term trends. This paper presents an automated method for selecting images for global scale lake mapping to minimize the influence of seasonality, while maintaining long term trends in lake surface area dynamics. Using historical meteorological data and a simple water balance model, we define the most stable period after the rainy season, when inflows equal outflows, independently for each Landsat tile and select images acquired during that ideal period for lake surface area mapping. The images selected using this method provide nearly complete global area coverage at decadal episodes for circa 2000 and circa 2014 from Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors, respectively. This method is being used in regional and global lake dynamics mapping projects, and is potentially applicable to any regional/global scale remote sensing application.

Neural-Dynamics-Driven Complete Area Coverage Navigation Through Cooperation of Multiple Mobile Robots

Multiple robots collaboratively achieve a common coverage goal efficiently, which can improve work capacity, share coverage tasks, and reduce completion time. In this paper, a neural dynamics (ND) approach is proposed for complete area coverage navigation by multiple robots. A bioinspired neural network (NN) is designed to model the workspace and guide a swarm of robots for the coverage mission. The dynamics of each neuron in the topologically organized NN is characterized by an ND equation. Each mobile robot regards other robots as moving obstacles. Each robot path is autonomously generated from the neural activity landscape of the NN and the previous robot position. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation, comparison studies, and experiments.

The Optimal Placement Pattern for Confident Information Coverage in Wireless Sensor Networks

Finding the optimal placement pattern to provide complete area coverage is of great theoretical and practical significance. Most of the current studies on placement problem are based on the simplest disk coverage model. In this paper, we study the optimal placement pattern based on the confident information coverage (CIC or $\Phi$ -coverage) model [1] , which is much more complicated than the disk coverage model. Based on the reconstruction theory, the CIC model takes into consideration of not only the collaboration of sensors for information processing but also the spatial correlation of physical phenomena. We first analyze $\Phi$ -coverage of one sensor and two sensors, and then $\Phi$ -coverage of $n$ sensors for $n \ge 3$ , where $n$ sensors are deployed at $n$ vertices of a regular $n$ -sided polygon. We prove that the regular triangular lattice is the optimal placement pattern among all the placement patterns consisting of regular polygons. We also extend our analysis to acute cyclic polygons, and prove that the regular triangular lattice is still the optimal placement pattern among all the placement patterns consisting of acute cyclic polygons.

Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach

Area coverage is an important issue in wireless sensor networks, which reflects how well an area is monitored or tracked by sensors. But, since a sensor network has restricted energy resources, energy efficiency is vital for this area coverage. One of the most efficient solutions to provide energy-aware area coverage is scheduling. That is, without any assumption about sensors' locations, only a distributed and parallel scheduling method determines which sensors should be on and which ones should be off in each decision period. The ultimate objective is to maximize network lifetime and keeping a target level of area coverage. A major part of the algorithms proposed in this field, schedule a sensor node activity based on its neighbors' information. Such information includes the distances of a node from its neighbors, the number of its active neighbors, etc. Indeed, message exchange is essential in the implementation of these algorithms which causes to increased energy consumption. In this paper, we propose a distributed scheduling algorithm, at which, each node itself decides to make its sensor on or off based on its location information and the node density over the target area. For this purpose, we first compute the minimum number of nodes that are enough to cover the target area. Then we obtain the best locations for theses nodes. Based on these computed location the area is partitioned into some sub-area, each one coverable by only one sensor. Then in each sub-area, a local scheduling procedure schedules the activation order of sensor. Simulation results show that the proposed algorithm, called CAOP, can maximize the network lifetime while maintaining complete area coverage.

A Distributed Homological Approach to Location-Independent Area Coverage in Wireless Sensor Networks

Area coverage is one of the fundamental problems in wireless sensor networks. The main objective is to minimize the number of active sensors to conserve energy consumption, while complete area coverage is achieved. Some of the existing solutions are based on the location information of sensors which are not applicable to scenarios where GPS modules are not available and there is no reliable location information of sensors. In this paper, we present a distributed homological sensor selection algorithm, namely DHSS, to select the least number of sensors to cover the entire area in the case that no location information is available. We consider the Rips complex of the network and formulate the selection problem as an optimization problem. DHSS tries to find the least number of 2-simplices of the Rips complex of the network to cover the entire area in a distributed manner. Finally, we evaluate the performance of the proposed algorithm by conducting simulation experiments.

Design of an Archimedes’ spiral based node deployment scheme targeting enhancement of network lifetime in wireless sensor networks

Wireless sensor networks (WSNs) have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, unbalanced energy consumption is an intrinsic problem and this can considerably decrease network lifetime. One primary way of mitigating uneven energy consumption is judicious deployment of sensor nodes so that the energy flow remains balanced throughout the network. In this work, at first we analyze layered network architecture and find out the conditions that need to be satisfied for optimal deployment setting in order to achieve complete area coverage, energy balance and prolonged network lifetime. We also analyze two standard distribution functions to find out their suitability as node deployment function and found neither of the distribution functions ensures balanced energy consumption, thereby failing to prolong network lifetime. Keeping the conditions of optimal node deployment setting in mind, and considering the reasons of unsuitability of the two standard distributions mentioned above, we identify Archimedes’ spiral based on which a node deployment function is proposed. Furthermore, we show the proposed deployment function provides optimal deployment setting and derive the constraint for balanced energy consumption, thereby subsequent enhancement of network lifetime. Exhaustive simulation is performed primarily to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing and maintaining coverage. We also measure the effect of placement errors on the performance of the scheme and show that even in presence of placement error the performance is comparable with the other competing schemes. Further, the simulation results show that our scheme does not compromise with other network performance metrics such as end-to-end delay and throughput while achieving the design goal. Finally, all the results are compared with three competing schemes and the results confirm our scheme’s supremacy in terms of both design performance metrics as well as network performance metrics.

Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates

Knowledge of soil properties with complete area coverage is needed for policy-making, land resource management, and monitoring environmental impacts. Remote sensing offers possibilities to support Digital Soil Mapping, especially in data-poor regions. The aim of this work was to test the potential of time-series of MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation and drought indices to provide relevant information to model topsoil properties in a Boreal-Atlantic region (Scotland) focussing on differentiation between soils with high and soils with low organic matter contents. For each of the three considered years, 345 MODIS data sets were included in the exploratory analysis; 15 data products for 23 dates (bi-weekly) per year. Terrain parameters derived from Shuttle Radar Topography Mission were also included. A methodology was implemented to exploit fully the high number of covariates, to identify the band, index or product that best correlates with the soil property of interest. In particular the proposed approach i. relies on freely globally available data-sets; ii. uses statistical criteria to select the combination of covariates providing the highest predictive capability, among the data considered and available; iii. deals with both continuous (using Generalized Additive Models, GAMs) and multinomial categorical (using Random Trees) types of variables; iv. takes into account fully the spatial autocorrelation of the data; v. provides estimates of the spatial uncertainty for each pixel; and vi. is computationally efficient when compared with methods such as forward stepwise. The models fitted show a fairly good agreement with existing data sets, presenting a consistent spatial pattern. The use of MODIS data as covariates increased the predictive capabilities of GAMs using only terrain parameters. The misclassification error for organic matter classes was between 25 and 35%. The assessment provided of the spatial uncertainty of the modelled values can be used in further modelling and in the assessment of consequences of climate-change and trade-off in land use changes. This approach can contribute to improving our understanding and modelling of soil processes and function over large, and relatively sparsely sampled, areas of the world.