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

Le, Anh Vu; Ku, Ping-Cheng; Tun, Thein Than; Nhân, Nguyễn Hữu; Shi, Yuyao; Mohan, Rajesh Elara

doi:10.3390/en12061136

Cited by 33 publications

(21 citation statements)

References 21 publications

(38 reference statements)

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“…After selecting the desired motors, power saving can be made by selecting the prescribed path and fixing the speed. If a robot visits different places in the fixed boundary, the path can be pre-programmed to reduce the total traveled distance (just as traveling salesperson problem, TSP) [28][29][30] and to avoid sudden start and stop movements, since, for typical DC motor starting current is high. Barili et al [4] discussed the concept of tuning the speed to save energy for the robot.…”

Section: Energy Aware Methodology For Htetro Operationmentioning

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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Section: Energy Aware Methodology For Htetro Operationmentioning

confidence: 99%

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

Manimuthu

Mohan

et al. 2019

Energies

Self Cite

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“…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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“…Research has also been conducted to provide heuristic-based algorithms [37] and distributed planning algorithms [38] for lattice-type inter-reconfigurable robots that are less architecture-specific. The hTetro robot platform, developed by Veerajagadheswar et al [39] is a chained-type inter-reconfigurable cleaning robot with seven potential configurations [40] which utilizes tileset theory to perform area coverage tasks with the awareness of energy consumption [41], [42], [43]. To carry out PP tasks on a hTetro platform, the algorithm should determine a valid path while taking several additional criteria into consideration including time consumption, path safety, and path smoothness.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot

Elara

Nhan

et al. 2020

IEEE Access

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Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstaclerich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning. INDEX TERMS Reconfigurable robot, Tiling robotics, Multi-objective path planning, Genetic algorithm, NSGA-II.

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

Cited by 33 publications

References 21 publications

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

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

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot

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