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Gabriely, Y.; Rimon, Elon

doi:10.1023/a:1016610507833

Cited by 257 publications

(21 citation statements)

References 26 publications

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“…As discussed in Section 1 the proposed CPP algorithm is a development of the heuristic STC algorithm [21,22] in a rule-based approach using reasoning techniques with hedge algebras to find the optimal CPP. Figure 2 models the proposed approach to enable effective CPP.…”

Section: The Methodologymentioning

confidence: 99%

“…The proposed CPP algorithm is a development of the heuristic spanning tree coverage (STC) algorithm [21] which "subdivides the work-area into disjoint cells corresponding to the square-shaped tool, then follows a spanning tree of the graph induced by the cells, while covering every point precisely once ... then follows a spanning tree of the graph induced by the cells, while covering every point precisely once" [22].…”

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confidence: 99%

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Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Hai

Moore

2018

Machines

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Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.

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Section: The Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Hai

Moore

2018

Machines

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“…The individual genes are decomposed into a number of individual phenotypes. Then, individual path length(Si) is calculated, so: (10) where n indicates the number of cells, while d(nj, nj+1) denotes the distance between Cells j and j + 1. Individuals with higher length(Si) will be eliminated with algorithms.…”

Section: Dynamic Inver-over Evolutionary Algorithmmentioning

confidence: 99%

“…Choset [3] proposed a classification of these algorithms into "online" and "offline" algorithms. In [4][5][6][7][8][9][10][11], some feasible methods are presented, such as cell decomposition, grid decomposition, spanning tree covering and random collision methods, whereas these methods are only suited to uninhabited areas, as they give no consideration to dynamic obstacles of environment. Under uncertain dynamic environments, there are a variety of intelligent methods such as neural network method [12,13] and genetic algorithm [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

et al. 2019

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A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.

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“…In this type of setup, a captured map is treated as multiple areas, and each area contains a value that describes the target environment, stating whether an obstacle is present or whether there is an unoccupied space. There are numerous algorithms that realize a grid-based area coverage approach, such as hexagonal grid decomposition [5], wavefront algorithm [6], the neural network-based area coverage algorithm in Reference [3], and the spanning tree method [7]. A graph-based area coverage path planning approach was presented by Xu et al in [8], where the mapped region is considered as a graph, and robot motion planning is applied to reach every point in the graph.…”

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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Cited by 257 publications

References 26 publications

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

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

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