arXiv:2603. 23405v2 Announce Type: replace-cross Abstract: Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms.
arXiv:2608. 06702v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints.
arXiv:2609.39559v1 Announce Type: new Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produ...
arXiv:2606. 15797v1 Announce Type: new Abstract: Compilation-based techniques represent an important stream of solvers for multi-agent path finding (MAPF) due to their modularity and adaptability for non-standard variants of the problem.
arXiv:2608. 05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones.
arXiv:2607. 04124v1 Announce Type: cross Abstract: Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do.
The paper presents a theoretical analysis of the Rolling‑Horizon Collision Resolution (RHCR) framework for Lifelong Multi‑Agent Path Finding (L‑MAPF), proving its near‑optimality in a discounted MDP setting. Building on this, the authors introduce Group Decentralized RHCR (GD‑RHCR), which partitions agents via a transitive communication scheme and plans each partition in parallel, achieving similar optimality guarantees while reducing per‑plan computational cost. Experiments across various maps demonstrate that GD‑RHCR scales to higher agent counts with high throughput and lower cost compared to vanilla RHCR.
The paper introduces Belief-Aware Multi-Agent Path Finding under Map Uncertainty, addressing the challenge that real-world environments can change unexpectedly. It proposes MAGIC, a framework that uses a Gaussian Markov Random Field and Gaussian Belief Propagation to update a shared belief about traversability online, allowing agents to infer the state of nearby unobserved locations. Experiments on standard MAPF benchmarks show that MAGIC reduces the executed sum of costs on 96.3% of instances, outperforming existing approaches across various planner families and large agent teams.
arXiv:2508. 19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i.
arXiv:2603.08814v2 Announce Type: replace-cross Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
Physical Agentic AI proposes an architecture that links semantic planning with physical execution for robot crews. Each robot exposes a typed skill library, while a foundation model planner decomposes tasks into phases and assigns robot‑skill pairs. A Robot Orchestrator validates and authorizes one skill at a time, ensuring actions are grounded in robot capabilities, system state, and workflow constraints before actuation.
arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
arXiv:2608. 15175v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making.