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Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

https://doi.org/10.3390/electronics13050944

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Improvement of K-means Cluster Quality by Post Processing Resulted Clusters

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UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

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Visual Coverage Path Planning for Urban Environments

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Morse Decompositions for Coverage Tasks

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Potential applications of unmanned ground and aerial vehicles to mitigate challenges of transport and logistics-related critical success factors in the humanitarian supply chain

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BIOINSPIRED NEURAL NETWORK-BASED Q-LEARNING APPROACH FOR ROBOT PATH PLANNING IN UNKNOWN ENVIRONMENTS

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CPPNet: A Coverage Path Planning Network

inferredcites →https://doi.org/10.25236/ajcis.2021.040506openalex-snapshot

K-means clustering algorithm: a brief review

inferredcites →https://doi.org/10.3389/frai.2021.740817openalex-snapshot

Adaptive Initialization Method for K-Means Algorithm

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Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments

inferredcites →https://doi.org/10.4271/2023-01-7065openalex-snapshot

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A bi-level joint scheduling model for charging and delivery of mobile charging robots serving for electric vehicles on highway networks under peak demand during holidays

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H3CPP: Boundary-Safe Coverage Path Planning for Multi-USV Operations in Irregular Coastal Environments

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inferred← citeshttps://doi.org/10.1109/icra57385.2026.11696337openalex-snapshot

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FSM-CCPP: Finite State Machine Based Complete Coverage Path Planning on Hexagonal Grids for Multi-Robot Systems

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Analysis and Research on Color Encoding and K-means Clustering Algorithm in Lingnan Landscape Design

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Reliable Learning-Enabled Multi-Robot Coverage with Masked Entity-Attention MAPPO: A Controlled Multi-Seed Study

inferred← citeshttps://doi.org/10.3390/app152011211openalex-snapshot

An Improved Multi-UAV Area Coverage Path Planning Approach Based on Deep Q-Networks

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Collaborative Coverage Path Planning for AUV Formations: Dual-Layer PSO-Voronoi Partitioning for Time-Based Load Balancing Combined with BINN

inferred← citeshttps://doi.org/10.3390/automation6010012openalex-snapshot

Intelligent Robot in Unknown Environments: Walk Path Using Q-Learning and Deep Q-Learning

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Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

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Decentralized Shared Actor–Critic Learning for Collision-Aware Small-Team Multi-Robot Coverage

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Graph-Gated Relational Reasoning for Enhanced Coordination and Safety in Distributed Multi-Robot Systems: A Decentralized Reinforcement Learning Approach

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Full-Coverage Path Planning for Heterogeneous UUVs Using a Hybrid Detection Point Layout and a Dual-Chromosome Co-Evolutionary Genetic Algorithm

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Coverage and Transition Planning in Obstacle-Rich Workspaces

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