Coverage Area Performance Research Articles

Internal Rehearsals for a Reconfigurable Robot to Improve Area Coverage Performance

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.

Long-term trials for improvement of autonomous area coverage with a Tetris inspired tiling self-reconfigurable system

In the past two decades, robotics have invested tremendous research effort to deploy long-term autonomous robots to ensure a smart hygienic manufacturing environment in the food and drink industry and translated such attempts into successful robotic products. However, most such systems were developed with fixed morphological design, which highly constrains their navigation and access in constraint spaces. To this end, we have developed hTetro, Tetris-inspired self-reconfigurable floor-sweeping system capable of performing autonomously in a constrained environment for an extended period to maximize the cleaning area. This paper presents the hTetro robot’s improved system design, control system, and autonomous coverage strategy that supports achieving long-term deployment. We validated the performance of the proposed system by conducting trials in a complex food court environment as our case study. Our trials at SUTD Lab, SUTD Singapore canteen, and Tampines Singapore food center are divided into three phases from Jan 2020 until the end of Sep 2020. We tracked the robot’s movement every day during each trial while cleaning to generate the coverage map, which helps compute the total area covered. Also, we systematically benchmarked the robot’s coverage performance with a commercially available fixed morphology robot cleaning robot in the same environment. To our best knowledge, this is the first time the reconfigurable robot has been deployed to validate the feasibility in terms of mechanical designs, electronics, and software for long-term autonomy in the real environment. The result exhibits that after fixing the issues during the platform development state, our hTetro robot could achieve maximum area coverage performance every day during the long-term trial in a real environment than the commercial robot due to its shape-shifting ability. With the successful deployment of the reconfigurable cleaning robot, a further important step towards service robotics for daily use has been taken.

Modelling and Control of a Reconfigurable Robot for Achieving Reconfiguration and Locomotion with Different Shapes.

Area coverage is a crucial factor for a robot intended for applications such as floor cleaning, disinfection, and inspection. Robots with fixed shapes could not realize an adequate level of area coverage performance. Reconfigurable robots have been introduced to overcome the limitations of fixed-shape robots, such as accessing narrow spaces and cover obstacles. Although state-of-the-art reconfigurable robots used for coverage applications are capable of shape-changing for improving the area coverage, the reconfiguration is limited to a few predefined shapes. It has been proven that the ability of reconfiguration beyond a few shapes can significantly improve the area coverage performance of a reconfigurable robot. In this regard, this paper proposes a novel robot model and a low-level controller that can facilitate the reconfiguration beyond a small set of predefined shapes and locomotion per instructions while firmly maintaining the shape. A prototype of a robot that facilitates the aim mentioned above has been designed and developed. The proposed robot model and controller have been integrated into the prototype, and experiments have been conducted considering various reconfiguration and locomotion scenarios. Experimental results confirm the validity of the proposed model and controller during reconfiguration and locomotion of the robot. Moreover, the applicability of the proposed model and controller for achieving high-level autonomous capabilities has been proven.

Wireless Sensor Network Path Optimization Using Sensor Node Coverage Area Calculation Approach

The proposed work is based on the path optimization approach for wireless sensor network (WSN). Path optimization is achieved by using the NSG 2.1 Tool, TCL Script file and NS2 simulator to improve the quality of service (QoS). Path optimization approach finds best suitable path between sensor nodes of WSN. The routing approach is not only the solution to improve the quality but also improves the WSN performance. The node cardinally is taken under consideration using the ad-hoc on demand distance vector routing protocol mechanism. Ad hoc approach emphasize on sensor nodes coverage area performance along with simulation time. NSG 2.1 Tool calculates the sensor node packet data delivery speed which can facilitate inter-node communication successfully. An experimental result verified that the proposed design is the best possible method which can escape from slow network response while covering maximum sensor nodes. It achieves coverage support in sensor node deployment. The result outcomes show best path for transferring packet from one sensor node to another node. The coverage area of sensor node gives the percentage of average coverage ratio of each node with respect to the simulation time.

One Slope Model: Efforts to Optimize Wifi Networks in the Office of communication and informatics in Palembang

Information technology in the field of transmission that is currently developing, one of which is Wi-Fi. Wi-Fi devices provide user convenience in carrying out their activities. The quality of Wi-Fi network performance can be known by the reception of the signal received by the user. If the placement of an access point (AP) is done correctly, the network will be optimized. There are several propagation models in the room that can be used as a guideline in determining the placement of the AP, one slope model is a way to measure the average level of a building and only depends on the distance of the transmitter and receiver. This research was conducted in order to overcome the problem of Wi-Fi network area coverage at the Office of Communication and Information of the City of Palembang. This study conducted an experiment to change the layout of AP's placement, measure and calculate data in priority with the one slope model. The results of measurements and calculations carried out analysis and comparison in order to determine the results of the experiments conducted. The results of this study indicate that an attempt to change the AP layout with one slope model can overcome existing problems and get better Wi-Fi coverage area performance. In the calculation with the one slope model of the 2-trial access point placement results in a decrease and an increase in signal. The signal reduction occurred in experiment 1, whereas in experiment 2 (design 2) the signal increased by 1.46dBm.

HTetro-Infi: A Reconfigurable Floor Cleaning Robot With Infinite Morphologies

The development of floor cleaning robots is an emerging area in robotics. Maximizing the area coverage is a foremost mission for a floor cleaning robot. Reconfigurable floor cleaning robots outperform floor cleaning robots with fixed morphology in the aspect of area coverage. A reconfigurable robot should be more flexible in changing its morphologies by considering the shapes of objects occupied in an environment to gain more coverage. Nevertheless, the state of the art methods of tiling robots considers only a limited number of morphologies for the reconfiguration, which is not sufficient to match the shape of an object. Therefore, this paper proposes a novel method to synthesize an appropriate morphology for a reconfigurable robot in accordance with the shape of an object. The proposed concept is named hTetro-Infi since it is not limited to a finite number of morphologies. The major novelty of the proposed concept overt the state of the art is the consideration of an infinite number of morphologies for the reconfiguration without sticking into a limited number of morphologies. Feedforward Neural Network (FNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for determining the hinge angle required for synthesizing a given morphology. Different configurations of FNNs and ANFISs were trained and evaluated to find the most suitable configurations. The area coverage performance of the proposed hTetro-Infi was compared against that of the state of the art methods of an existing class of tiling robots, which considers only a limited number of morphologies, through simulations. According to the statistical conclusions, the proposed hTetro-Infi is capable of significantly improving area coverage compared to an existing tiling-theory based floor cleaning robot. Furthermore, the area coverage improvement of hTetro-Infi is noteworthy. Therefore, the proposed concept is beneficial in improving the abilities of a reconfigurable cleaning robot. Real-world experiments with the hardware platform of the robot for evaluating the performance is expected to be conducted in the next phase of the work. Furthermore, consideration of hTetro-Infi for navigation through confined areas is proposed for future work.

A COMPARISON OF SINGLE PHOTON AND FULL WAVEFORM LIDAR

Abstract. Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2–3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10 cm.

Optimum Deployment of Multiple UAVs for Coverage Area Maximization in the Presence of Co-Channel Interference

The use of the unmanned aerial vehicle (UAV) as the aerial base stations can provide wireless communication services in the form of UAV-based small cells (USCs). Thus, the major design challenge that needs to be addressed is the coverage maximization of such USCs in the presence of co-channel interference generated by multiple UAVs operating within a specific target area. Consequently, the efficient deployment strategy is imperative for USCs while optimizing the coverage area performance to compensate for the impact of interference. To this end, this paper presents a coordinated multi-UAV strategy in two scenarios. In the first scenario, symmetric placement of UAVs is assumed at a common optimal altitude and transmit power. In the second scenario, asymmetric deployment of UAVs with different altitudes and transmit powers is assumed. Then, the coverage area performance is investigated as a function of the separation distance between UAVs that are deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell boundary. Finally, the system-level performance of a boundary user is studied in terms of the coverage probability. The numerical results unveil that the SINR threshold, the separation distance, and the number of UAVs and their formations should be carefully selected to achieve the maximum coverage area inside and to reduce the unnecessary expansion outside the target area. Thus, this paper provides important design guidelines for the deployment of multiple UAVs in the presence of co-channel interference.

Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor.

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Floor cleaning robot with reconfigurable mechanism

Abstract Although several precedents have demonstrated the benefits of using floor cleaning robots for the maintenance of constructed buildings, traditional platforms suffer from a series of performance issues. One major factor that contributes to their performance deficit is their fixed morphology design, which highly constrains their navigation and access. To this end, a novel Tetris-inspired reconfigurable floor cleaning robot called hTetro is presented in this paper. The developed robot is capable of reconfiguring its morphology to any of seven one-sided tetrominoes in response to its perceived environment to maximize its coverage area. This paper presents the coverage area performance of the hTetro robot and systematically benchmarks its performance with two commercially available fixed morphology robot platforms. Our experiments indicate that hTetro achieves significantly higher coverage performance than the other platforms due to its shape-shifting ability in response to navigating its environment.

Tackling Area Coverage Problems in a Reconfigurable Floor Cleaning Robot Based on Polyomino Tiling Theory

Whilst Polyomino tiling theory has been extensively studied as a branch of research in mathematics, its application has been largely confined to multimedia, graphics and gaming domains. In this paper, we present a novel application of Tromino tiling theory, a class of Polyomino with three cells in the context of a reconfigurable floor cleaning robot, hTromo. The developed robot platform is able to automatically generate a global tiling set required to cover a defined space while leveraging on the Tromino tiling theory. Specifically, we validated the application of five Tromino tiling theorems with our hTromo robot. Experiments performed clearly demonstrate the efficacy of the proposed approach resulting in very high levels of area coverage performance in all considered experimental cases. This paper also presents the system architecture of our hTromo robot and a detailed description of the five tiling theorems applied in this study.

A Tiling-Theoretic Approach to Efficient Area Coverage in a Tetris-Inspired Floor Cleaning Robot

Although numerous studies have focused on the development and application of polyomino tiling theories, research of this nature is typically limited to the graphics and gaming fields. In this paper, we are presenting an innovative application of the polyomino tiling theory which is applied to Tetris-inspired reconfigurable robotic cleaning device as a means of solving the area coverage problem. The robotic floor cleaner (hTetro) that was developed as part of this research leverages the polyomino tiling theory to automatically generate the global tiling set required to ensure the full area of a given space is covered. In this paper, three Tetris tiling theorems were validated using our developed hTetro robot. The results of the research clearly indicated that the proposed approach offers a strong area coverage performance across all experimental cases. This paper includes an outline of the system architecture that underpins the hTetro robot and a comprehensive overview of the three tiling theorems that were applied in this research.

Active visual SLAM for robotic area coverage: Theory and experiment

This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area within a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM’s navigation estimate will inevitably drift over time in the absence of loop closures. Therefore, efficient area coverage and good SLAM navigation performance represent competing objectives. To solve this decision-making problem, we introduce perception-driven navigation, an integrated navigation algorithm that automatically balances between exploration and revisitation using a reward framework. This framework accounts for SLAM localization uncertainty, area coverage performance, and the identification of good candidate regions in the environment for visual perception. Results are shown for both a hybrid simulation and real-world demonstration of a visual SLAM system for autonomous underwater ship hull inspection.