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

Veerajagadheswar, Prabakaran; Elara, Mohan Rajesh; Sivanantham, Vinu; Pathmakumar, Thejus; Kumar, Suganya Sampath

doi:10.3390/app8030342

Cited by 17 publications

(20 citation statements)

References 30 publications

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“…When the workspace is divided into several grid cells then it is called a complete grid path planning (CGPP) or a complete grid coverage (CGC) [1,2]. It has a wide area of applications including warehouse application, floor cleaning, environment mapping, surveillance, search and rescue operation [1,3,4]. Different methods were presented in the literature to address the complete grid coverage problem.…”

Section: Introductionmentioning

confidence: 99%

Neural dynamics based complete grid coverage by single and multiple mobile robots

2021

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Navigation of mobile robots in a grid based environment is useful in applications like warehouse automation. The environment comprises of a number of free grid cells for navigation and remaining grid cells are occupied by obstacles and/or other mobile robots. Such obstructions impose situations of collisions and dead-end. In this work, a neural dynamics based algorithm is proposed for complete coverage of a grid based environment while addressing collision avoidance and dead-end situations. The relative heading of the mobile robot with respect to the neighbouring grid cells is considered to calculate the neural activity. Moreover, diagonal movement of the mobile robot through inter grid cells is restricted to ensure safety from the collision with obstacles and other mobile robots. The circumstances where the proposed algorithm will fail to provide completeness are also discussed along with the possible ways to overcome those situations. Simulation results are presented to show the effectiveness of the proposed algorithm for a single and multiple mobile robots. Moreover, comparative studies illustrate improvements over other algorithms on collision free effective path planning of mobile robots within a grid based environment.

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Section: Introductionmentioning

confidence: 99%

Neural dynamics based complete grid coverage by single and multiple mobile robots

2021

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“…In contrast with t-trominoes [23], the tetromino clustered tile sets can cover the maximum area with less number of reconfigurable shapes.…”

Section: Comparison With T-trominoesmentioning

confidence: 99%

“…Besides, any 4 × N grid can be tiled through clustered tetromino pattern of (4 × 4), (4 × 5), (4 × 6) and 4 × 7 sets. But the grids size of (3 × 5), (3 × 7), and (3 × 9) cannot be covered by any arrangements of t-trominoes tile sets [23]. Hence, tetromino patterns have more flexibility in covering the maximum area of the arbitrary shape.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…The same authors have proposed a tromino tiling theory-based motion planning technique for hTromo robot. The algorithm uses L-and I-trominoes pattern for generating the clustered tiling pattern [23]. Though the works mentioned above [18,23,24] discuss the entire area coverage using tetromino and tromino tiling algorithms, they do not address the selective dirt area coverage in a given workspace.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage

et al. 2018

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This paper proposes a visual dirt detection algorithm and a novel adaptive tiling-based selective dirt area coverage scheme for reconfigurable morphology robot. The visual dirt detection technique utilizes a three-layer filtering framework which includes a periodic pattern detection filter, edge detection, and noise filtering to effectively detect and segment out the dirt area from the complex floor backgrounds. Then adaptive tiling-based area coverage scheme has been employed to generate the tetromino morphology to cover the segmented dirt area. The proposed algorithms have been validated in Matlab environment with real captured dirt images and simulated tetrominoes tile set. Experimental results show that the proposed three-stage filtering significantly enhances the dirt detection ratio in the incoming images with complex floors with different backgrounds. Further, the selective dirt area coverage is performed by excluding the already cleaned area from the unclean area on the floor map by incorporating the tiling pattern generated by adaptive tetromino tiling algorithm.

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“…The microcontroller is placed in the second block of the robot where it is programmed to carry out three major functions: (1) To drive the dc motors based on current morphology and control its speed during locomotion using the generated control signals, (2) To control the servo motors in full duplex mode by sending the control signals by angular rotation and receiving the feedback angle from the servo motor, (3) Establishing Human-robot interaction by receiving the user commands through Bluetooth serial communication protocol. The whole robot is powered using a Lithium Polymer battery which is rated with 7.4 V and 900 mAh capacity [26] as shown in Figure 2. A flip switch connected with the main power supply branch empowers making and breaking of series and parallel circuits.…”

Section: Htetro Hardware Architecturementioning

confidence: 99%

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

Manimuthu

Mohan

et al. 2019

Energies

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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.

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

Cited by 17 publications

References 30 publications

Neural dynamics based complete grid coverage by single and multiple mobile robots

Neural dynamics based complete grid coverage by single and multiple mobile robots

Vision-Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage

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

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