End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition

Abstract: Efficient Coverage Path Planning (CPP) is necessary for autonomous robotic lawnmowers to effectively navigate and maintain lawns with diverse and irregular shapes. This paper introduces a comprehensive end-to-end pipeline for CPP, designed to convert user-defined boundaries on an aerial map into optimized coverage paths seamlessly. The pipeline includes user input extraction, coordinate transformation, area decomposition and path generation using our novel AdaptiveDecompositionCPP algorithm, preview and customization through an interactive coverage path visualizer, and conversion to actionable GPS waypoints. The AdaptiveDecompositionCPP algorithm combines cellular decomposition with an adaptive merging strategy to reduce non-mowing travel thereby enhancing operational efficiency. Experimental evaluations, encompassing both simulations and real-world lawnmower tests, demonstrate the effectiveness of the framework in coverage completeness and mowing efficiency.

• The paper introduces an end-to-end coverage planning system that transforms user-defined boundaries and converts paths to GPS waypoints.
• The proposed AdaptiveDecompositionCPP algorithm optimizes mowing by merging cells to reduce non-mowing travel and ensure up to 97.2% coverage.
• The framework's simulation and interactive visualization validate its practical viability for real-world robotic lawnmower applications.

An In-depth Analysis of the CPP Framework for Robotic Lawnmowers

The paper presents a detailed exposition of an end-to-end coverage path planning (CPP) framework tailored for autonomous robotic lawnmowers. This research significantly advances the domain by introducing the AdaptiveDecompositionCPP algorithm, which incorporates a novel approach to cellular decomposition by merging strategies for enhanced operational efficiency. The framework aims to address the intricate challenges posed by irregular lawn shapes and obstacles, ensuring not only complete coverage but also optimizing for aesthetics and efficiency.

Summary of the Methodology

The proposed framework is robust in its approach to CPP, integrating several key components:

1. User-Input Extraction and Transformation: Users define boundaries on an aerial interface, which are then transformed into local coordinates to facilitate computational processes.
2. Adaptive Decomposition and Merging: The pivotal AdaptiveDecompositionCPP algorithm leverages cellular decomposition combined with an adaptive merging strategy. The algorithm considers different angles of decomposition, optimizing for the fewest sections by merging sections to minimize turns and non-mowing travel. This is crucial to improving lawnmower operational efficiency and aesthetics by reducing abrupt turns and overlaps.
3. Simulation and Visualization: An interactive coverage path visualizer is developed, allowing users to modify and preview mowing parameters, such as boundary offsets and turning radii, enhancing the practical usability of the pipeline.
4. Conversion to GPS Waypoints: The finalized paths are converted to GPS coordinates, making them implementable by the autonomous mower, thereby completing the end-to-end flow.

Comparative Analysis with Existing Methods

The framework is positioned against established CPP methods, such as Trapezoidal and Boustrophedon Decomposition and grid-based solutions. The paper's experimental results reveal:

• Improved coverage percentages, with up to 97.2% coverage achieved, compared to Boustrophedon and other methods.
• Significant reduction in non-mowing distances, attributed to the effectiveness of the merging strategy, achieving better efficiency than traditional methods.
• Lower distance per coverage metrics, a critical measure of efficiency in path planning.

The results underscore the superiority of the proposed method in balancing efficiency and coverage while minimizing operational complexities.

Theoretical and Practical Implications

Theoretically, this research underscores the importance of optimizing path planning metrics beyond mere coverage, integrating geometric and operational constraints to enhance real-world applicability. The adaptive decomposition and merging approach offer a new paradigm for energy-efficient path planning in automated systems, with potential applications extending beyond lawnmowers to other field robotics contexts.

Practically, the framework translates seamlessly to real-world scenarios, including experimental validation through implemented tests on medium-sized lawnmowers. The seamless transition from simulation to real-world execution highlights the framework's robustness and viability, offering a significant stride forward in autonomous lawn management.

Future Directions

While the framework presents a substantial leap in the field of robotic lawnmowers, future research could focus on enhancing localization precision through advanced sensor fusion techniques and exploring multi-agent coordination for larger-scale implementations. Additionally, further refinement of the section-merging strategies could yield even better optimization results in diverse and complex lawn geometries.

In conclusion, the paper provides a comprehensive, adaptable, and efficient solution for coverage path planning in robotic lawnmowers, making noteworthy contributions to both theory and practice in the field of mobile robotics.