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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

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

Jaël Champagne Gareau¹, Éric Beaudry¹, Vladimir Makarenkov¹

¹GDAC-LIA, Computer Science Department, Université du Québec à Montréal, Montréal, QC, Canada.

Frontiers in Robotics and AI

|February 23, 2023

Summary

This study presents an optimal algorithm for coverage path planning (CPP) that significantly outperforms exhaustive search methods. The new branch-and-bound approach efficiently finds complete coverage paths for complex grid environments.

Keywords:

branch-and-bound coverage path planning (CPP)heuristic search intelligent decision making iterative deepening depth-first search optimal solution pruning robotics

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Area of Science:

Robotics
Artificial Intelligence
Computational Geometry

Background:

Coverage Path Planning (CPP) is crucial for tasks like mapping and surveillance.
Existing exact CPP algorithms are often computationally prohibitive.

Purpose of the Study:

To develop an optimal and efficient algorithm for discrete grid-based CPP.
To introduce novel branch-and-bound strategies for improved CPP performance.

Main Methods:

Iterative Deepening Depth-First Search (ID-DFS) baseline.
Branch-and-bound strategies: Loop detection and Admissible heuristic function.
Evaluation on six novel benchmark grid types (Coast-like, Random links, Random walk, Simple-shapes, Labyrinth, Wide-Labyrinth).

Main Results:

The proposed algorithm finds optimal coverage paths.
Achieves performance orders of magnitude faster than exhaustive search CPP.
Demonstrates effectiveness on diverse and complex grid environments.

Conclusions:

The branch-and-bound algorithm offers an efficient and optimal solution for grid-based CPP.
This work presents the first exact general CPP algorithm beyond exhaustive search.
The tested benchmark grids have practical applications in various real-world scenarios.