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Open AccessArticle

Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots

1
ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
2
Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 445; https://doi.org/10.3390/s20020445
Received: 27 November 2019 / Revised: 23 December 2019 / Accepted: 8 January 2020 / Published: 13 January 2020
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: reconfigurable robot; cleaning robot; navigation planning; area coverage; energy aware; evolutionary algorithm reconfigurable robot; cleaning robot; navigation planning; area coverage; energy aware; evolutionary algorithm
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MDPI and ACS Style

Le, A.V.; Nhan, N.H.K.; Mohan, R.E. Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots. Sensors 2020, 20, 445.

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