A solution to room-by-room coverage for autonomous cleaning robots

Kleiner, Alexander; Baravalle, Rodrigo; Kolling, Andreas; Pilotti, Pablo; Munich, Mario E.

doi:10.1109/iros.2017.8206429

Cited by 26 publications

(13 citation statements)

References 10 publications

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“…The area coverage for a given environment is the most vital factor for a floor cleaning robot [28,38]. A method to segment rooms based on an occupancy grid map has been proposed to improve the area coverage of a floor cleaning robot [16]. The ability of the proposed method in improving area coverage has been validated using an off-the-shelf floor cleaning robot.…”

Section: Introductionmentioning

confidence: 99%

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Samarakoon

Muthugala

et al. 2021

Complex Intell. Syst.

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Complete area coverage is a crucial factor for a floor cleaning robot. Self-reconfigurable tiling robots have been introduced over robots with a fixed shape for floor cleaning since they improve the area coverage by the flexibility of shape-shifting in cluttered environments. The existing coverage methods of reconfigurable tiling robots follow the tiling theory to cope with the area coverage problem. However, these methods merely consider a limited set of predefined shapes for the reconfiguration of a robot. The consideration of a limited set of predefined shapes for the reconfiguration impedes the ability of coverage to a certain extent in typical floor environments. Therefore, this paper proposes a novel method to improve area coverage of a tiling robot by reconfiguring according to the shape of obstacles. To this end, the required hinge angles for reconfiguring per the shape of an obstacle are determined by a genetic algorithm. The proposed method considers an optimized shape for reconfiguration in lieu of a limited set of predefined shapes. The coverage improvement of the proposed concept has been compared against the existing coverage methods of tiling robots to validate the performance. According to the experimental results, the proposed method surpasses the existing coverage methods of tiling robots from the perspective of area coverage, and the improvement is significant and noteworthy.

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Section: Introductionmentioning

confidence: 99%

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Samarakoon

Muthugala

et al. 2021

Complex Intell. Syst.

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“…However, it achieves poor performance when the shape of the room is not very regular. The distance transform watershed-based segmentation method [ 11 , 12 , 13 , 14 ] divides the room by applying a distance transform to find the room centers at optimal threshold, then segmenting with a wavefront propagation. Besides, the lidar point clouds information and laser scanning data are also widely used to classify and segment the maps [ 15 , 16 , 17 ], but because of its high computational complexity, it’s still a big challenge for low memory consumption devices like the Raspberry Pi.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, Kleiner, A. [ 14 ] proposed a method can get the rooms and doorways semantic labels of the map, but they are labeled by a human via a cloud service and phone/tablet APP.…”

Section: Introductionmentioning

confidence: 99%

Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Zheng

Duan

Wang

et al. 2021

Sensors

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Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.

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“…To that end, the focus has been on developing wheeled robots [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], flying robots [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], and snakelike robots [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Many recent studies focus on developing not only robots that perform risky or difficult tasks for humans, but also production robots in workplaces that face labor shortages [ 27 , 28 , 29 , 30 ], cleaning robots to support housework to meet the demands of our busy lives [ 31 , 32 , 33 , 34 , 35 ], and robots to support the medical sector. Besides fixed robots (e.g., arm robots), most robots move and perform tasks in a wide range of environments; if robots have a wide range of movement, their large size does not constitute a problem.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Spiral Motion by a Small-Type Robot on an Obstacle-Available Surface

Tokunaga

Premachandra

et al. 2021

Micromachines

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Several robot-related studies have been conducted in recent years; however, studies on the autonomous travel of small mobile robots in small spaces are lacking. In this study, we investigate the development of autonomous travel for small robots that need to travel and cover the entire smooth surface, such as those employed for cleaning tables or solar panels. We consider an obstacle-available surface and target this travel on it by proposing a spiral motion method. To achieve the spiral motion, we focus on developing autonomous avoidance of obstacles, return to original path, and fall prevention when robots traverse a surface. The development of regular travel by a robot without an encoder is an important feature of this study. The traveled distance was measured using the traveling time. We achieved spiral motion by analyzing the data from multiple small sensors installed on the robot by introducing a new attitude-control method, and we ensured that the robot returned to the original spiral path autonomously after avoiding obstacles and without falling over the edge of the surface.

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A solution to room-by-room coverage for autonomous cleaning robots

Cited by 26 publications

References 10 publications

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Autonomous Spiral Motion by a Small-Type Robot on an Obstacle-Available Surface

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