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

TL;DR

This paper introduces CoPCS, a learning-based method enabling heterogeneous UAV-UGV teams to perform synchronized, concurrent co-planning under operational constraints, significantly enhancing multi-robot collaboration in complex tasks.

Contribution

We propose CoPCS, a novel end-to-end learning approach that integrates graph transformers for task encoding and joint co-planning, facilitating synchronized multi-robot operations under constraints.

Findings

CoPCS enables synchronized concurrent co-planning in UAV-UGV teams.

Experimental results show improved team performance in simulations and real robots.

The method effectively handles energy and traversability constraints in multi-robot planning.

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.