Collaborative Planning with Concurrent Synchronization for Operationally Constrained UAV-UGV Teams

2026-03-06

AI Summary

This work addresses the challenge of coordinating heterogeneous unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) under energy limitations and terrain constraints in complex missions. To this end, the authors propose CoPCS, a novel method that achieves, for the first time, simultaneous and concurrent co-planning for UAV–UGV teams. Built upon an end-to-end imitation learning framework, CoPCS employs a heterogeneous graph Transformer to encode task constraints and leverages a Transformer decoder to jointly optimize multi-UAV task assignment and multi-UGV path planning, thereby unifying collaborative decision-making with operational constraints. Experimental results demonstrate that CoPCS significantly enhances overall system performance on both simulated and real-world platforms, enabling efficient and sustained execution of cooperative tasks.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Collaborative planning under operational constraints is an essential capability for heterogeneous robot teams tackling complex large-scale real-world tasks. Unmanned Aerial Vehicles (UAVs) offer rapid environmental coverage, but flight time is often limited by energy constraints, whereas Unmanned Ground Vehicles (UGVs) have greater energy capacity to support long-duration missions, but movement is constrained by traversable terrain. Individually, neither can complete tasks such as environmental monitoring. Effective UAV-UGV collaboration therefore requires energy-constrained multi-UAV task planning, traversability-constrained multi-UGV path planning, and crucially, synchronized concurrent co-planning to ensure timely in-mission recharging. To enable these capabilities, we propose Collaborative Planning with Concurrent Synchronization (CoPCS), a learning-based approach that integrates a heterogeneous graph transformer for operationally constrained task encoding with a transformer decoder for joint, synchronized co-planning that enables UAVs and UGVs to act concurrently in a coordinated manner. CoPCS is trained end-to-end under a unified imitation learning paradigm. We conducted extensive experiments to evaluate CoPCS in both robotic simulations and physical robot teams. Experimental results demonstrate that our method provides the novel multi-robot capability of synchronized concurrent co-planning and substantially improves team performance. More details of this work are available on the project website: https://hcrlab.gitlab.io/project/CoPCS.

Problem

• collaborative planning
• operational constraints
• UAV-UGV teams
• synchronized co-planning
• heterogeneous robots

Innovation

• concurrent co-planning
• heterogeneous graph transformer
• synchronized collaboration
• operationally constrained planning
• imitation learning