Lantao Liu

About

Lantao Liu is a robotics and autonomous systems researcher whose work spans multi-robot coordination, environmental monitoring, and intelligent decision-making under uncertainty. His early contributions established foundational methods for multi-robot task allocation, including dynamic partitioning strategies for large-scale systems (83 citations) and interval-based algorithms for handling uncertain utility estimates (67 citations). He further advanced market-based allocation mechanisms through rigorous optimality analysis, bridging the gap between heuristic design and provable performance guarantees (49 citations). A distinctive thread in Liu's research is the deployment of autonomous vehicles—particularly AUVs and ASVs—for aquatic environmental sampling, where he pioneered data-driven learning and adaptive planning approaches (80 citations) and developed multi-robot persistent monitoring frameworks (45 citations). His Pareto Monte Carlo Tree Search method (50 citations) introduced principled multi-objective informative planning for robots balancing exploration and exploitation under time constraints. More recently, Liu has addressed spatiotemporal decision-making through time-varying Markov Decision Processes (43 citations), autonomous navigation in cluttered environments via Log-MPPI control (38 citations), and convex optimization frameworks for team-based task allocation (36 citations). Collectively accumulating over 500 citations, his body of work meaningfully advances both the theory and practice of intelligent multi-robot systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Large-scale multi-robot task allocation via dynamic partitioning and distribution

   83 citations · 2012
2. 2
   Data‐driven learning and planning for environmental sampling

   80 citations · 2017
3. 3
   Assessing optimal assignment under uncertainty: An interval-based algorithm

   67 citations · 2011
4. 4
   Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning

   50 citations · 2019
5. 5
   Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

   49 citations · 2013
6. 6
   Multi-robot Informative and Adaptive Planning for Persistent Environmental Monitoring

   45 citations · 2018
7. 7
   A Solution to Time-Varying Markov Decision Processes

   43 citations · 2018
8. 8
   Autonomous Navigation of <i>AGV</i>s in Unknown Cluttered Environments: <i>Log-MPPI</i> Control Strategy

   38 citations · 2022
9. 9
   Multitask Allocation Framework With Spatial Dislocation Collision Avoidance for Multiple Aerial Robots

   37 citations · 2022
10. 10
    A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

    36 citations · 2023

Key Collaborators

Contact & Links