Fast and Optimal Planner for the Discrete Grid-Based Coverage Path-Planning Problem

Gareau, Jaël Champagne; Beaudry, Éric

doi:10.1007/978-3-030-91608-4_9

Cited by 1 publication

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“…This article is an extended version of our conference paper presented at the IDEAL2021 meeting (Champagne Gareau et al, 2021b). We extend our previous work by:…”

Section: Introductionmentioning

confidence: 92%

“…This article is an extended version of our conference paper presented at the IDEAL2021 meeting ( Champagne Gareau et al., 2021b ). We extend our previous work by: • greatly expanding the Introduction and Related Work sections to present various applications of CPP and to better show how our algorithm (including the two proposed improvements) compares to the existing approaches; • improving the proposed heuristic function; • improving the empirical evaluation, including additionnal experiments, new plots and two new type of benchmark grids; • expanding the analysis of our results, by explaining more deeply the causes of the observed improvements and what these results mean in practice.…”

Section: Introductionmentioning

confidence: 99%

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Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

Gareau

Beaudry²

2023

Front. Robot. AI

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This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.

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“…This article is an extended version of our conference paper presented at the IDEAL2021 meeting (Champagne Gareau et al, 2021b). We extend our previous work by:…”

Section: Introductionmentioning

confidence: 92%