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.
By Alex DeWeese, Jiaoyang Li, Guannan Qu
arXiv:2610.03709v1 Announce Type: cross
Abstract: We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication...
By Kuangyu Ding, Gesualdo Scutari
The paper proposes a method that allows agents in multi‑agent motion planning to compute with multiple prioritizations simultaneously, avoiding reliance on domain‑specific heuristics or costly iterative searches. This general approach is applied to receding‑horizon motion planning with system dynamics, and numerical experiments show near‑optimal prioritization and performance gains over state‑of‑the‑art methods with only a slight increase in computation time. Real‑time feasibility is demonstrated on a road network with ten vehicles in a Cyber‑Physical Mobility Lab.
By Patrick Scheffe, Julius Kahle, Bassam Alrifaee
arXiv:2611.07528v1 Announce Type: new
Abstract: LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate succ...
By Stephen Chang, Daniel Harabor
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...
By Oren Salzman
The paper introduces Pivot-and-Station Multi-Agent Path Finding (PS‑MAPF), a variant of MAPF where a subset of agents must visit interchangeable pivots before all agents occupy anonymous stations. It provides a full solvability characterization: every instance on a 2‑edge‑connected graph is solvable, and for arbitrary connected graphs a structural effective‑distance measure relative to unoccupied vertices gives a necessary and sufficient condition. The authors prove that minimizing station‑makespan or station‑flowtime is NP‑hard even with a single pivot, and present three algorithms—a complete baseline, a SAT‑based optimal solver, and Pivot‑Prioritized Planning (PPP), which solves 74‑89% of benchmark instances with significantly lower makespan and flowtime than the baseline.
By Andrea Di Nezza, Mihir Patel, Fabio Fagnani, Sara Bernardini