On the Complete Coverage Path Planning for Mobile Robots

Hsu, Ping-Min; Lin, Chun-Liang; Yang, Mengyao

doi:10.1007/s10846-013-9856-0

Cited by 22 publications

(8 citation statements)

References 19 publications

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“…Motion-planning algorithms can be divided into two parts. If all the data for the robot's environment are known and available at the start, then global motion-planning algorithms can be used to generate a collision-free path [1], [2]. However, if the robot can only use local sensor-based information about its surrounding dnese and dynamic environment, then reactive motion-planning algorithms can provide an acceptable solution for generating the robot's path and velocity [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Uncertain estimation-based motion-planning algorithms for mobile robots

Gyenes

Szadeczky-Kardoss

2021

ACTA IMEKO

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Collision-free motion planning for mobile agents is a challenging task, especially when the robot has to move towards a target position in a dynamic environment. The main aim of this paper is to introduce motion-planning algorithms using the changing uncertainties of the sensor-based data of obstacles. Two main algorithms are presented in this work. The first is based on the well-known velocity obstacle motion-planning method. In this method, collision-free motion must be achieved by the algorithm using a cost-function-based optimisation method. The second algorithm is an extension of the often-used artificial potential field. For this study, it is assumed that some of the obstacle data (e.g. the positions of static obstacles) are already known at the beginning of the algorithm (e.g. from a map of the enviroment), but other information (e.g. the velocity vectors of moving obstacles) must be measured using sensors. The algorithms are tested in simulations and compared in different situations.

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Section: Introductionmentioning

confidence: 99%

Uncertain estimation-based motion-planning algorithms for mobile robots

Gyenes

Szadeczky-Kardoss

2021

ACTA IMEKO

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“…20 Similarly, Hameed et al proposed a genetic algorithm for CPP and used boustrophedon paths for coverage. 26 A time and energy-efficient online CPP technique are proposed in Hsu et al, 27 wherein they adopt a high-resolution grid map representation and utilized spiral path motion to perform adequate coverage. In another work, Gabriely and Rimon applied the spiral motion in a cellular decomposition coverage technique.…”

Section: Introductionmentioning

confidence: 99%

Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

Veerajagadheswar

Elara

et al. 2020

International Journal of Advanced Robotic Systems

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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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“…In relation to spiral motion, Gabriel et al used spiral motion in a cellular decomposition coverage technique [22]. In another work, a more energy-and time-efficient online coverage path planning technique is presented in [23], where they adopt a high-resolution grid map representation and utilize spiral path motion to perform efficient coverage. In works related to backtracking spiral motion, E. Gonzalez et al utilized backtracking spiral motion in a cellular decomposition area coverage method [24].…”

Section: Introductionmentioning

confidence: 99%

Complete Path Planning for a Tetris-Inspired Self-Reconfigurable Robot by the Genetic Algorithm of the Traveling Salesman Problem

Manimuthu

Veerajagadheswar

et al. 2018

Electronics

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The efficiency of autonomous systems that tackle tasks such as home cleaning, agriculture harvesting, and mineral mining depends heavily on the adopted area coverage strategy. Extensive navigation strategies have been studied and developed, but few focus on scenarios with reconfigurable robot agents. This paper proposes a navigation strategy that accomplishes complete path planning for a Tetris-inspired hinge-based self-reconfigurable robot (hTetro), which consists of two main phases. In the first phase, polyomino form-based tilesets are generated to cover the predefined area based on the tiling theory, which generates a series of unsequenced waypoints that guarantee complete coverage of the entire workspace. Each waypoint specifies the position of the robot and the robot morphology on the map. In the second phase, an energy consumption evaluation model is constructed in order to determine a valid strategy to generate the sequence of the waypoints. The cost value between waypoints is formulated under the consideration of the hTetro robot platform’s kinematic design, where we calculate the minimum sum of displacement of the four blocks in the hTetro robot. With the cost function determined, the waypoint sequencing problem is then formulated as a travelling salesman problem (TSP). In this paper, a genetic algorithm (GA) is proposed as a strong candidate to solve the TSP. The GA produces a viable navigation sequence for the hTetro robot to follow and to accomplish complete coverage tasks. We performed an analysis across several complete coverage algorithms including zigzag, spiral, and greedy search to demonstrate that TSP with GA is a valid and considerably consistent waypoint sequencing strategy that can be implemented in real-world hTetro robot navigations. The scalability of the proposed framework allows the algorithm to produce reliable results while navigating within larger workspaces in the real world, and the flexibility of the framework ensures easy implementation of the algorithm on other polynomial-based shape shifting robots.

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On the Complete Coverage Path Planning for Mobile Robots

Cited by 22 publications

References 19 publications

Uncertain estimation-based motion-planning algorithms for mobile robots

Uncertain estimation-based motion-planning algorithms for mobile robots

Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

Complete Path Planning for a Tetris-Inspired Self-Reconfigurable Robot by the Genetic Algorithm of the Traveling Salesman Problem

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