Papers
27
Total Citations
1,294
H-Index
17
About
Hang Ma is a leading researcher in multi-agent systems and autonomous robotics, with a focus on Multi-Agent Path Finding (MAPF) — the challenge of coordinating collision-free movement for large numbers of agents simultaneously. His work has fundamentally shaped how the field defines, benchmarks, and solves these problems, as evidenced by his 2021 paper "Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks," which has accumulated 276 citations and remains a cornerstone reference for researchers entering the domain. Ma's contributions extend well beyond theoretical foundations. He has tackled critical real-world gaps in MAPF research, including kinematic constraints that govern how physical robots actually move, payload transfer logistics in warehouse automation, and lifelong planning for continuous pickup-and-delivery operations. His 2016 papers on kinematic constraints and package-exchange routing (each with over 135 citations) demonstrated early on his commitment to bridging AI planning and practical robotics deployment. More recently, Ma has embraced reinforcement learning approaches, exploring decentralized, communication-aware policies for partially observable environments. With over 1,000 cumulative citations across his most impactful work, his research continues to influence both academia and industry applications in automated warehouses and large-scale autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks276 citations · 2021
- 2Multi-Agent Path Finding with Kinematic Constraints139 citations · 2016
- 3
- 4Distributed Heuristic Multi-Agent Path Finding with Communication102 citations · 2021
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- 7Task and Path Planning for Multi-Agent Pickup and Delivery85 citations · 2019
- 8Summary: Multi-Agent Path Finding with Kinematic Constraints52 citations · 2017
- 9
- 10Graph-Based Multi-Robot Path Finding and Planning40 citations · 2022