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

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

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Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420914441 ◽

2020 ◽

Vol 17 (2) ◽

pp. 172988142091444 ◽

Cited By ~ 8

Author(s):

Prabakaran Veerajagadheswar ◽

Ku Ping-Cheng ◽

Mohan Rajesh Elara ◽

Anh Vu Le ◽

Masami Iwase

Keyword(s):

Path Planning ◽

Motion Planning ◽

Robotic Systems ◽

Planning Techniques ◽

Essential Ingredient ◽

Coverage Path Planning ◽

Cleaning Robot ◽

Simulated Environment ◽

Navigation Path ◽

Planning Technique

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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R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Remote Sensing ◽

10.3390/rs13081525 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1525

Author(s):

Gang Tang ◽

Congqiang Tang ◽

Hao Zhou ◽

Christophe Claramunt ◽

Shaoyang Men

Keyword(s):

Path Planning ◽

Google Earth ◽

Simulation Experiments ◽

Minimum Width ◽

Coverage Path Planning ◽

Optimal Paths ◽

Planning Approach ◽

Polygonal Area ◽

Aerial Vehicle ◽

Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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Complete coverage path planning for road cleaning robot

2010 International Conference on Networking, Sensing and Control (ICNSC) ◽

10.1109/icnsc.2010.5461585 ◽

2010 ◽

Cited By ~ 2

Author(s):

Guoqing Hu ◽

Zhengwei Hu ◽

Hongbo Wang

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Coverage Path Planning ◽

Cleaning Robot

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Dynamic coverage path planning and obstacle avoidance for cleaning robot based on behavior

2011 International Conference on Electric Information and Control Engineering ◽

10.1109/iceice.2011.5777555 ◽

2011 ◽

Cited By ~ 3

Author(s):

Liang Yuming ◽

Wen Ruchun ◽

Zhang Zhenli ◽

Zhu Junlin

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Coverage Path Planning ◽

Cleaning Robot

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An Energy Efficient Coverage Path Planning Approach for Mobile Robots

Advances in Intelligent Systems and Computing - Intelligent Computing ◽

10.1007/978-3-030-01177-2_28 ◽

2018 ◽

pp. 387-397

Author(s):

Amna Khan ◽

Iram Noreen ◽

Zulfiqar Habib

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Energy Efficient ◽

Coverage Path Planning ◽

Planning Approach

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Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Robotica ◽

10.1017/s0263574718000292 ◽

2018 ◽

Vol 36 (8) ◽

pp. 1144-1166 ◽

Cited By ~ 9

Author(s):

Héctor Azpúrua ◽

Gustavo M. Freitas ◽

Douglas G. Macharet ◽

Mario F. M. Campos

Keyword(s):

Path Planning ◽

Autonomous Vehicles ◽

Autonomous Robots ◽

Mineral Exploration ◽

Statistical Investigation ◽

Coverage Path Planning ◽

Simulated Environment ◽

Outdoor Setting ◽

Geophysical Surveys ◽

Multi Robot

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

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