A Fast Electric Vehicle Planner Using Clustering

Gareau, Jaël Champagne; Beaudry, Éric

doi:10.1007/978-3-030-60104-1_3

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The field of automated planning (sometimes called AI planning ) focuses on finding a sequence of actions that allows an intelligent agent (for example, a robot) to reach a goal state (for example, a specific position in the environment) from a given initial state ( Ghallab et al., 2016 ). An example of a real-world application of automated planning is the problem of finding an optimal path for an electric vehicle ( Champagne Gareau et al., 2021a ). Another problem studied in automated planning is the complete Coverage Path-Planning (CPP) problem, where the objective is to find an optimal or quasi-optimal path that covers every area in the region (we call such a path a complete coverage path of the region).…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

Self Cite

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

Gareau

Beaudry

2021

Intelligent Data Engineering and Automated Learning – IDEAL 2021

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PcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains

Gareau,

Beaudry,

Makarenkov

2023

Studies in Classification, Data Analysis, and Knowledge Organization

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Markov Decision Processes (MDPs) are useful to solve real-world probabilistic planning problems. However, finding an optimal solution in an MDP can take an unreasonable amount of time when the number of states in the MDP is large. In this paper, we present a way to decompose an MDP into Strongly Connected Components (SCCs) and to find dependency chains for these SCCs. We then propose a variant of the Topological Value Iteration (TVI) algorithm, called parallel chained TVI (pcTVI), which is able to solve independent chains of SCCs in parallel leveraging modern multicore computer architectures. The performance of our algorithm was measured by comparing it to the baseline TVI algorithm on a new probabilistic planning domain introduced in this study. Our pcTVI algorithm led to a speedup factor of 20, compared to traditional TVI (on a computer having 32 cores).

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A Fast Electric Vehicle Planner Using Clustering

Cited by 3 publications

References 7 publications

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

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

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

PcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains

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