Scalable Multi-Robot Task Allocation and Coordination under Signal Temporal Logic Specifications

2025-03-04

AI Summary

To address the scalability challenge in large-scale multi-robot systems for complex temporal task allocation and coordinated control, this paper proposes a hierarchical planning framework grounded in Signal Temporal Logic (STL). The method explicitly encodes STL constraints over discrete path assignments and progress variables—rather than continuous states—to avoid combinatorial explosion. It integrates sampling-based single-robot path planning, STL semantic encoding, mixed-integer linear programming (MILP) for global optimization, and distributed local trajectory tracking. Evaluated in simulations with up to 100 robots, the approach efficiently synthesizes trajectories satisfying stringent spatiotemporal logic specifications (e.g., “sequentially visit region A within 5 seconds, while at most two robots occupy region B at any time”). The framework ensures global task satisfaction while achieving both scalability and real-time tractability.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Motion planning with simple objectives, such as collision-avoidance and goal-reaching, can be solved efficiently using modern planners. However, the complexity of the allowed tasks for these planners is limited. On the other hand, signal temporal logic (STL) can specify complex requirements, but STL-based motion planning and control algorithms often face scalability issues, especially in large multi-robot systems with complex dynamics. In this paper, we propose an algorithm that leverages the best of the two worlds. We first use a single-robot motion planner to efficiently generate a set of alternative reference paths for each robot. Then coordination requirements are specified using STL, which is defined over the assignment of paths and robots' progress along those paths. We use a Mixed Integer Linear Program (MILP) to compute task assignments and robot progress targets over time such that the STL specification is satisfied. Finally, a local controller is used to track the target progress. Simulations demonstrate that our method can handle tasks with complex constraints and scales to large multi-robot teams and intricate task allocation scenarios.

Problem

• Scalable multi-robot task allocation under STL specifications
• Efficient motion planning with complex STL constraints
• Coordination of large multi-robot systems using MILP

Innovation

• Single-robot motion planner generates reference paths
• STL specifies coordination over path assignments
• MILP computes task assignments and progress targets