Collaborative Planning with Concurrent Synchronization for Operationally Constrained UAV-UGV Teams
Authors: Zihao Deng, Qianhuang Li, Peng Gao, Maggie Wigness, John Rogers, Donghyun Kim, Hao Zhang
Organizations: Zihao Deng, Qianhuang Li, Donghyun Kim, and Hao Zhang are with the University of Massachusetts Amherst, Amherst, MA, 01002, USA. · Peng Gao is with North Carolina State University, Raleigh, NC, 27695, USA. · Maggie Wigness and John Rogers are with the U.S. Army DEVCOM Army Research Laboratory (ARL), Adelphi, MD, 20783, USA.
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.
Figures & tables
Fig. 1: A motivating scenario of a UAV-UGV team performing collaborative planning under operational constraints to visit all task points for environmental monitoring. UAVs are flight-time limited due to energy constraints, while UGVs are constrained by terrain traversability, preventing either platform from completing the mission alone. To overcome this, UGVs act as mobile recharging stations, carrying energy supplies and synchronizing with UAVs to sustain their operations. Task points are shown as white circles and recharging points as green circles, while colored lines denote UAV trajectories and black lines denote UGV paths.
Fig. 2: Overview of CoPCS ( Collaborative Planning with Concurrent Synchronization ), a unified learning-based method that integrates a heterogeneous graph transformer for synchronized collaboration among operationally constrained UAV-UGV teams and a transformer decoder for joint action co-planning. The CoPCS approach is trained end-to-end, and its co-planning policy is executed concurrently to coordinate all the robots, which enables concurrent synchronization.
Fig. 3: Qualitative results of CoPCS in a map-based simulator. The first four figures illustrate a mission with 15 tasks and 5 paths, while the last four figures show a mission with 45 tasks and 10 paths. The experiments evaluate different team configurations, ranging from 1 UAV and 1 UGV to 4 UAVs and 2 UGVs. Task points are depicted as white circles, recharging points as green circles, UAV trajectories as colored lines, and UGV paths as black lines.
Fig. 4: Qualitative results of co-planning with concurrent synchronization for a team of two Warthog UGVs and two quadrotor UAVs in a high-fidelity Unity-based 3D simulator integrated with ROS1. The mission includes 45 tasks and 10 traversable paths. The first column of the two subfigures illustrates the team starting configuration and a synchronized recharging event during execution, while the remaining subfigures depict trajectories at different timestamps.
Fig. 5: Qualitative results illustrating the generalizability of CoPCS in unseen environments. The large-scale suburban and urban maps are not used during training and also contain variations in starting points and road network layouts. In the top environment, a 2-UAV and 2-UGV team is required to visit 15 tasks, while in the bottom environment, a 4-UAV and 2-UGV team is required to visit 15 tasks.
Fig. 6: Qualitative results of CoPCS in real-world mixed-reality experiments using two Crazyflie UAVs and two Limo UGVs controlled through ROS2.
Khoury Department of Computer Sciences, Northeastern University, Boston, MA · The Charles Stark Draper Laboratory, Inc., Cambridge, MA · Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA +1
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA · Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering, Northeastern University, Boston, MA, USA