Graph Theory-Based Approach to Accomplish Complete Coverage Path Planning Tasks for Reconfigurable Robots

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.

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

Energies ◽

10.3390/en12061136 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1136 ◽

Cited By ~ 15

Author(s):

Anh Le ◽

Ping-Cheng Ku ◽

Thein Than Tun ◽

Nguyen Huu Khanh Nhan ◽

Yuyao Shi ◽

...

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Energy Aware ◽

Coverage Path Planning ◽

Differential Drive ◽

Reconfigurable Robots ◽

Cleaning Robot ◽

Sequencing Strategy ◽

Planning Strategies ◽

Four Blocks

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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Complete Coverage Path Planning for Intelligent Sweeping Robot

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) ◽

10.1109/ipec51340.2021.9421172 ◽

2021 ◽

Author(s):

Bing Luo ◽

Yueqin Huang ◽

Fuqin Deng ◽

Weike Li ◽

Yong Yan

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Coverage Path Planning

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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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Complete coverage path planning and guidance for cleaning robots

ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics ◽

10.1109/isie.1997.649051 ◽

2002 ◽

Cited By ~ 57

Author(s):

R.N. De Carvalho ◽

H.A. Vidal ◽

P. Vieira ◽

M.I. Ribeiro

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Coverage Path Planning ◽

Cleaning Robots

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Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

International Journal of Distributed Sensor Networks ◽

10.1155/2012/567959 ◽

2012 ◽

Vol 8 (10) ◽

pp. 567959 ◽

Cited By ~ 7

Author(s):

Mingzhong Yan ◽

Daqi Zhu ◽

Simon X. Yang

Keyword(s):

Path Planning ◽

Real Time ◽

Autonomous Underwater Vehicle ◽

Evidence Theory ◽

Sensor Data ◽

Map Building ◽

Complete Coverage ◽

Coverage Path Planning ◽

Underwater Environment ◽

Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

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Toward energy-efficient online Complete Coverage Path Planning of a ship hull maintenance robot based on Glasius Bio-inspired Neural Network

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115940 ◽

2021 ◽

pp. 115940

Author(s):

M.A. Viraj J. Muthugala ◽

S.M. Bhagya P. Samarakoon ◽

Mohan Rajesh Elara

Keyword(s):

Neural Network ◽

Path Planning ◽

Energy Efficient ◽

Ship Hull ◽

Complete Coverage ◽

Coverage Path Planning

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An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1377 ◽

2011 ◽

Vol 467-469 ◽

pp. 1377-1385 ◽

Cited By ~ 4

Author(s):

Ming Zhong Yan ◽

Da Qi Zhu

Keyword(s):

Path Planning ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Time Varying ◽

Simulation Studies ◽

Complete Coverage ◽

Biologically Inspired ◽

Coverage Path Planning ◽

Underwater Environment ◽

Global Cost

Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.

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