Reinforcement Learning-Based Energy-Aware Area Coverage for Reconfigurable hRombo Tiling Robot

Le, Anh Vu; Parween, Rizuwana; Kyaw, Phone Thiha; Elara, Mohan Rajesh; Minh, Tran Hoang Quang; Borusu, Charan Satya Chandra Sairam

doi:10.1109/access.2020.3038905

Cited by 28 publications

(20 citation statements)

References 37 publications

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“…The ability to change their morphology helps them to achieve this objective. There are several reconfigurable tiling robots developed by researchers from the ROAR laboratory to perform cleaning and other tasks [ 17 , 18 , 19 ]. The robots mentioned here can morph into different shapes.…”

Section: Introductionmentioning

confidence: 99%

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

Apuroop

Elara

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

“…The robots mentioned here can morph into different shapes. The robot from [ 17 ] can morph into seven shapes, and the robots from [ 18 , 19 ] can morph into three shapes each. In the current study, the modified hTrihex, a honeycomb-shaped tiling robot, was utilized to test our proposed scheme.…”

Section: Introductionmentioning

confidence: 99%

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

Apuroop

Elara

et al. 2021

Sensors

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“…The first is a robot that comprises a group of intelligent modules, and the second is a robot that comprises of a group of modules where the intelligence comes from an external agent. In our previous work, we have classified self-reconfigurable robots into three categories which are nested reconfigurability [ 16 , 17 , 18 ], intra reconfigurability [ 19 , 20 ], and inter reconfigurability [ 21 , 22 ]. The hTetro described earlier has nested reconfigurability.…”

Section: Introductionmentioning

confidence: 99%

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Ramalingam

et al. 2021

Sensors

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Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot’s width is possible, due to the scissor mechanism at the core of the robot’s body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.

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“…With the advanced high speed and accuacy devices, the captured 3D object [15] have been applied in suveliance applicaitons for quality assessmen. Moreover, the exiting CCTV system [16], [17], [18] consisting of monocular RGB cameras is not cover the whole the workspace and CCTV is hard to detect the 3D human attributes. In the works of [19], the authors have addressed the distance-time encounter patterns in a crowd that allows the fixed surveillance system to identify social groups, such as families, by imposing adaptive thresholds on the distance-time contact patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Robotics and several other autonomous technologies in AGV vehicles have made great strides in fighting the COVID-19 pandemic [22], [23]. The service robot uses the AI-assisted technology to deliver the medicine to covid 19 patients, safe entry checkin body temperature measurement, sanitize the infected area, frequent cleaning of high touchpoints like hospital walls, floor, and door handle [24], [25], [16]. Hence, by considering the advantage of service robots and AI-assisted technology, as well as the flexible navigation in the vast and complex environments such as shopping mall, food court, wet market, resents robot system gradually replace human in the tedious jobs.…”

Section: Introductionmentioning

confidence: 99%

Social Density Monitoring Toward Selective Cleaning by Human Support Robot With 3D Based Perception System

Ramalingam

Gómez

et al. 2021

IEEE Access

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Monitoring the safe social distancing then conducting efficient sterilization in potentially crowded public places are necessary but challenging especially during the COVID-19 pandemic. This work presents the 3D human space-based surveillance system enabling selective cleaning framework. To this end, the proposed AI-assisted perception techniques is deployed on Toyota Human Support Robot (HSR) equipped with autonomous navigation, Lidar, and RGBD vision sensor. The human density mapping represented as heatmap was constructed to identify areas with the level being likely the risks for interactions. The surveillance framework adopts the 3D human joints tracking technique and the accumulated asymmetrical Gaussian distribution scheme modeling the human location, size, and direction to quantify human density. The HSR generates the human density map as a grid-based heatmap to perform the safe human distance monitoring task while navigating autonomously inside the pre-built map. Then, the cleaning robot uses the levels of the generated heatmap to sterilize by the selective scheme. The experiment was tested in public places, including food court and wet market. The proposed framework performance analyzed with standard performance metrics in various map sizes spares about 19 % of the disinfection time and 15 % of the disinfection liquid usage, respectively.

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Reinforcement Learning-Based Energy-Aware Area Coverage for Reconfigurable hRombo Tiling Robot

Cited by 28 publications

References 37 publications

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

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

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Social Density Monitoring Toward Selective Cleaning by Human Support Robot With 3D Based Perception System

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