add preprints of lsmart, mggo, and mdpibt

_data/preprints.yml

@@ -15,7 +15,53 @@
 #    arXiv: null
 #  abstract: null
 
-- key: Yan25
+
+- key: Jiang2026MDPIBT
+  title: "Planning over MAPF Agent Dependencies via Multi-Dependency PIBT"
+  # site: https://yulunzhang.net/publication/zhang2026mggo/
+  authors: [Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li]
+  equal_contributions: [Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni]
+  venue: arXiv
+  year: 2026
+  thumbnail: /files/yulunzhang/thumbnails/simple_bigagents_MDPIBT.gif
+  eprint: arXiv:2603.23405
+  tags: [mapf, warehouse]
+  links:
+    arXiv: https://arxiv.org/abs/2603.23405
+  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. Priority Inheritance with Backtracking (PIBT) is a popular algorithm capable of effectively planning in such situations. However, PIBT is constrained by its rule-based planning procedure and lacks generality because it restricts its search to paths that conflict with at most one other agent. This limitation also applies to Enhanced PIBT (EPIBT), a recent extension of PIBT. In this paper, we describe a new perspective on solving MAPF by planning over agent dependencies. Taking inspiration from PIBT's priority inheritance logic, we define the concept of agent dependencies and propose Multi-Dependency PIBT (MD-PIBT) that searches over agent dependencies. MD-PIBT is a general framework where specific parameterizations can reproduce PIBT and EPIBT. At the same time, alternative configurations yield novel planning strategies that are not expressible by PIBT or EPIBT. Our experiments demonstrate that MD-PIBT effectively plans for as many as 10,000 homogeneous agents under various kinodynamic constraints, including pebble motion, rotation motion, and differential drive robots with speed and acceleration limits. We perform thorough evaluations on different variants of MAPF and find that MD-PIBT is particularly effective in MAPF with large agents."
+
+
+- key: Zhang2026MGGO
+  title: "Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding"
+  site: https://yulunzhang.net/publication/zhang2026mggo/
+  authors: [Yulun Zhang, Varun Bhatt, Matthew C. Fontaine, Stefanos Nikolaidis, Jiaoyang Li]
+  venue: arXiv
+  year: 2026
+  thumbnail: /files/yulunzhang/thumbnails/mgg.png
+  eprint: arXiv:2602.23468
+  tags: [warehouse, envopt]
+  links:
+    arXiv: https://arxiv.org/abs/2602.23468
+  abstract: "Multi-Agent Path Finding (MAPF) aims to move agents from their start to goal vertices on a graph. Lifelong MAPF (LMAPF) continuously assigns new goals to agents as they complete current ones. To guide agents' movement in LMAPF, prior works have proposed Guidance Graph Optimization (GGO) methods to optimize a guidance graph, which is a bidirected weighted graph whose directed edges represent moving and waiting actions with edge weights being action costs. Higher edge weights represent higher action costs. However, edge weights only provide soft guidance. An edge with a high weight only discourages agents from using it, instead of prohibiting agents from traversing it. In this paper, we explore the need to incorporate edge directions optimization into GGO, providing strict guidance. We generalize GGO to Mixed Guidance Graph Optimization (MGGO), presenting two MGGO methods capable of optimizing both edge weights and directions. The first optimizes edge directions and edge weights in two phases separately. The second applies Quality Diversity algorithms to optimize a neural network capable of generating edge directions and weights. We also incorporate traffic patterns relevant to edge directions into a GGO method, making it capable of generating edge-direction-aware guidance graphs."
+
+
+- key: YanAndZhang2026LSMART
+  title: "Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems"
+  site: https://smart-mapf.github.io/lifelong-smart/
+  authors: [Jingtian Yan, Yulun Zhang, Zhenting Liu, Han Zhang, He Jiang, Jingkai Chen, Stephen F. Smith, Jiaoyang Li]
+  equal_contributions: [Jingtian Yan, Yulun Zhang]
+  venue: arXiv
+  year: 2026
+  thumbnail: /files/yulunzhang/thumbnails/lsmart-logo-black-square.png
+  eprint: arXiv:2602.15721
+  tags: [mapf, warehouse, execution]
+  links:
+    arXiv: https://arxiv.org/abs/2602.15721
+    Code: https://github.com/smart-mapf/lifelong-smart
+  abstract: "We present Lifelong Scalable Multi-Agent Realistic Testbed (LSMART), an open-source simulator to evaluate any Multi-Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. However, SMART is designed for MAPF, not LMAPF. Generalizing SMART to an FMS requires many more design choices. First, an FMS parallelizes planning and execution, raising the question of when to plan. Second, given planners with varying optimality and differing agent-model assumptions, one must decide how to plan. Third, when the planner fails to return valid solutions, the system must determine how to recover. In this paper, we first present LSMART, an open-source simulator that incorporates all these considerations to evaluate any MAPF algorithms in an FMS. We then provide experiment results based on state-of-the-art methods for each design choice, offering guidance on how to effectively design centralized lifelong AGV Fleet Management Systems. LSMART is available at this https URL."
+
+
+- key: Yan2025SMART
   title: "Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)"
   site: https://jingtianyan.github.io/publication/2025-03-02-smart-testbed
   authors: [Jingtian Yan, Zhifei Li, William Kang, Kevin Zheng, Yulun Zhang, Zhe Chen, Yue Zhang, Daniel Harabor, Stephen F. Smith, Jiaoyang Li]