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

Le, Anh Vu; Veerajagadheswar, Prabakaran; Sivanantham, Vinu; Mohan, Rajesh Elara

doi:10.3390/s18082585

Cited by 139 publications

(84 citation statements)

References 25 publications

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“…In addition, most of the existing reconfigurable robot are not compatible for cleaning purpose due to the dependent locomotion (for instance, locomotion is performed by rolling the platform), cleaning module design (for instance, cleaning modules must be on multiple sides of the platform in case of rolling locomotion) and subsequently the size and capacity of locomotion and cleaning modules. On the other hand, the capability of hTetro to change its shape to seven tetromino morphologies O, Z, L, T, J, S, I and ability to implement the locomotion within each configuration are similar to our previous works of [28]. It is worth noting that in this work which benchmarked the performance of hTetro with fix form robots, we put great emphasis on the robots' capability of cleaning the entire room without leaving any uncleaned space behind by activating the cleaning modules operate continuously during robot navigation.…”

Section: Htetro Kinematic Design With Differential Drive Mechanismsupporting

confidence: 79%

“…To this end, this class of robot needs to perform locomotion and transformation tasks smoothly. The locomotion the hTetro robot platform in [28] of each block is being operated by four DC motors attached with omnidirectional wheels, and the transformation to seven tetromino shapes is executed by rotating to defined angles of servo motors at hinges connecting four robot blocs.…”

Section: Htetro Kinematic Design With Differential Drive Mechanismmentioning

confidence: 99%

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Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Tun

et al. 2019

Energies

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The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.

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Section: Htetro Kinematic Design With Differential Drive Mechanismsupporting

confidence: 79%

Section: Htetro Kinematic Design With Differential Drive Mechanismmentioning

confidence: 99%

Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Tun

et al. 2019

Energies

Self Cite

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“…The output is a two-dimensional activation map that provides the response of convolution at each spatial position. Based on the size of the filter and the size of the input, the size of the output can be determined using Equation (2).…”

Section: Cnn Based Litter Detection Frameworkmentioning

confidence: 99%

“…Due to long working hours, low wages, unwillingness to work as a cleaner, workforce shortage has been a constant problem for food court cleaning and maintenance tasks in recent times [1]. Recently, many robotic platforms are designed for different cleaning application which include floor cleaning [2,3], facade cleaning [4], staircase cleaning [5], pavement cleaning [6,7] and garden cleaning [8]. However, these robot architectures could not support table cleaning and maintenance tasks.…”

Section: Introductionmentioning

confidence: 99%

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Yin

Apuroop

Tamilselvam

et al. 2020

Sensors

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This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96% detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.Sensors 2020, 20, 1698 2 of 20 vision-based techniques are widely used in cleaning robots for recognizing the litter and compute the cleaning action [14][15][16][17][18][19]. Andersen et al., built up a visual cleaning map for cleaning robots using a vision algorithm and a powerful light-transmitting diode. The sensor recognizes the grimy region and generates the dirt map by examining the surface pictures pixel-by-pixel utilizing the multi-variable statistical method [15]. David et al., proposed high-level manipulation actions for cleaning dirt from table surfaces using REEM a humanoid service robot. The author uses a background subtraction algorithm for recognizing the dirt from the table and Noisy Indeterministic Deictic (NID) rules-based learning algorithm to generate the sequence of cleaning action [16]. Ariyan et al., developed a planning algorithm for the removal of stains from non-planar surfaces where the author uses a depth-first branch-and-bound search to generate cleaning trajectories with the K-means clustering algorithm [17]. Hass et al., demonstrated the use of unsupervised clustering algorithm and Markov Decision Problem (MDP) for performing the cleaning task where unsupervised clustering algorithm is used to distinguish the dirt from surface and MDP algorithm is used to generate the maps, and transition model from clustered image is used to describe the robot cleaning action [18]. Nonetheless, these approaches have some practical issues and disadvantages for using in food court table cleaning; the detection ratio relies heavily on the textured surfaces, which makes it challenging to identify the litter type as solid...

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“…One primary attributes that curtail the efficacy of the floor cleaning robots is their fixed morphology design, which restrains their accessibility in constrained spaces and weakening area coverage. We proposed an alternative design in the works of Prabakaran et al 6 and Le et al 7 to overcome these bottlenecks in the traditional cleaning platforms where we introduced a next-generation of reconfigurable floor cleaning robot named "hTetro" inspired from the game called Tetris. The developed robot is capable of reconfiguring its morphology to any of the seven one-sided Tetris pieces which aid the robot to achieve superior area coverage performance than a traditional fixed morphology cleaning platforms.…”

Section: Introductionmentioning

confidence: 99%

Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

Veerajagadheswar

Elara

et al. 2020

International Journal of Advanced Robotic Systems

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Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.

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

Cited by 139 publications

References 25 publications

Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

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