LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control

2025-07-15

AI Summary

To address collision, deadlock, and unreliable trajectory tracking in point-to-point navigation of multiple omnidirectional robots in known environments, this paper proposes a hierarchical asynchronous coordination architecture. At the high level, a centralized discrete multi-agent path-finding (MAPF) solver—based on an improved LaCAM algorithm—generates collision-free paths; at the low level, a distributed dynamics-aware feedback controller (the Freyja stack) executes continuous-time trajectory tracking. The core contribution is a paradigm shift from “coupled planning-and-control” to “coupled planning with decoupled control,” enabling dynamic replanning, asynchronous goal updates, and proactive deadlock avoidance. Evaluated on a real-world testbed comprising 15 heterogeneous aerial and ground robots, the system demonstrates high real-time performance, strong robustness under stochastic goal updates and human-induced disturbances, and favorable scalability.

Technology Category

• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots independently follow their assigned paths reliably. This hierarchical shift in planning representation from (i) discrete and coupled to (ii) continuous and decoupled domains enables the framework to maintain long-term scalable motion synthesis. As an instantiation of this idea, we present LF, which combines a fast state-of-the-art MAPF solver (LaCAM), and a robust feedback control stack (Freyja) for executing agile robot maneuvers. LF provides a robust and versatile mechanism for lifelong multi-robot navigation even under asynchronous and partial goal updates, and adapts to dynamic workspaces simply by quick replanning. We present various multirotor and ground robot demonstrations, including the deployment of 15 real multirotors with random, consecutive target updates while a person walks through the operational workspace.

Problem

• Solve point-to-point navigation for multiple holonomic robots
• Combine centralized path planning with decentralized trajectory control
• Enable scalable multi-robot navigation in dynamic environments

Innovation

• Centralized discrete full-horizon planner for collision-free paths
• Dynamics-aware optimal trajectory controllers for reliable path following
• Combines fast MAPF solver with robust feedback control