Multi-Robot Cooperative Path Planning: Advances, Challenges, and Future Trends
Bibliographic record
Abstract
From automated warehouse robots coordinating shelf movements to drones and ground vehicles navigating disaster zones, multi-robot systems (MRS) are transforming how complex tasks are performed in dynamic environments. Unlike single-robot systems, MRS can execute tasks in parallel, cover large areas efficiently, and adapt to unexpected changes. These advantages make them ideal for applications such as smart manufacturing, autonomous logistics, and emergency response. However, as robot teams scale up, they face growing challenges—task allocation becomes harder, path conflicts increase, and communication delays can undermine real-time coordination. This paper provides a structured review of cooperative path planning approaches for MRS. First, outline the fundamental characteristics of multi-robot coordination and key challenges such as collision avoidance, computational complexity, and dynamic re-planning. Then, categorize the main planning algorithms into three types: graph-based methods, intelligent optimization techniques, and deep learning models. We also compare centralized and distributed planning frameworks in terms of scalability, robustness, and real-world feasibility. Application case studies from warehouse systems, intelligent transportation, and search-and-rescue missions are presented to demonstrate how these planning strategies are implemented in practice. Finally, discuss open challenges such as heterogeneous robot integration, safety assurance, and communication resilience, and highlight future research directions including hybrid architectures and learning-driven coordination. This review aims to support the development of scalable, adaptive, and efficient path planning in multi-robot systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".