Energy-Aware Collaborative Exploration for a UAV-UGV Team

Cahit Ikbal Er; Saikiran Juttu; Yasin Yazicioglu

cs.ROMar 23, 2026

Energy-Aware Collaborative Exploration for a UAV-UGV Team

Authors: Cahit Ikbal Er, Saikiran Juttu, Yasin Yazicioglu

Organizations: Department of Mechanical and Industrial Engineering at Northeastern University, Boston, MA.

Abstract

We present an energy-aware collaborative exploration framework for a UAV-UGV team operating in unknown environments, where the UAV's energy constraint is modeled as a maximum flight-time limit. The UAV executes a sequence of energy-bounded exploration tours, while the UGV simultaneously explores on the ground and serves as a mobile charging station. Rendezvous is enforced under a shared time budget so that the vehicles meet at the end of each tour before the UAV reaches its flight-time limit. We construct a sparsely coupled air-ground roadmap using a density-aware layered probabilistic roadmap (PRM) and formulate tour selection over the roadmap as coupled orienteering problems (OPs) to maximize information gain subject to the rendezvous constraint. The resulting tours are constructed over collision-validated roadmap edges. We validate our method through simulation studies, benchmark comparisons, and real-world experiments.

Figures & tables

Fig. 1 : Incremental dual-layer PRM construction. (a-c) Top view of UAV layer construction (purple nodes). (d) 3D view showing ground layer generated through aerial node projection and independent ground sampling (green nodes), interconnected by rendezvous edges (dashed lines).

Fig. 2 : Consecutive exploration tours of the UAV and the UGV.

Fig. 3 : Environments used in the simulations.

Parameter	Value	Parameter	Value
$r$ (voxel resolution)	$0.1m$	UAV sensing range	$2.0m$
UAV horizontal FOV	$90^{\circ}$	UAV vertical FOV	$90^{\circ}$
UGV sensing range	$5.0m$	UGV horizontal FOV	$360^{\circ}$
UGV vertical FOV	$30^{\circ}$	$\overline{\tau_{a}}$ (Max flight time)	$120s$
$v_{uav}$ (UAV avg. speed)	$0.25\ m/s$	$v_{ugv}$ (UGV avg. speed)	$0.17\ m/s$
$\tau_{c}$ (charging duration)	$5.0s$	$\lambda$ (utility weight)	$0.001$

TABLE I : Simulation Parameters

Fig. 4 : Exploration progress across all environments. Bold lines show the mean and shaded regions indicate

\pm

1 standard deviation. The x-axis represents total mission time, including, computation and recharging time.

Env	$t_{\mathrm{exp}}^{80\%}$ (min)	$t_{\mathrm{exp}}^{95\%}$ (min)	$t_{\mathrm{comp}}^{95\%}$ (s)	$N_{tours}$
1	$8.12\pm 0.60$	$14.13\pm 1.28$	$3.65\pm 0.63$	$8.0\pm 0.7$
2	$9.97\pm 0.87$	$15.46\pm 1.90$	$4.70\pm 1.10$	$9.2\pm 0.8$
3	$5.23\pm 1.52$	$8.63\pm 0.65$	$1.86\pm 0.21$	$5.0\pm 0$

TABLE II : Simulation Results per Environment.

t_{\mathrm{exp}}^{80\%}

and

t_{\mathrm{exp}}^{95\%}

: active exploration time to reach 80% and 95% exploration.

t_{\mathrm{comp}}^{95\%}

: total computation time until 95% exploration.

N_{\mathrm{tours}}

: number of tours until termination.

Fig. 5 : Exploration in Environment 1 . (a) UGV exploring a tunnel inaccessible to the UAV, (b) UAV exploring an elevated scaffolding unreachable by the UGV, (c) a snapshot of the exploration when the UGV enters the tunnel

Fig. 6 : Exploration progress of Our Method vs TERRA vs UAV-only baseline. Bold lines are the means and shaded regions are

\pm

1 standard deviation.

	Ours			TERRA			UAV-only
Env	$t_{\mathrm{exp}}^{80\%}$ (min)	$t_{\mathrm{exp}}^{95\%}$ (min)	$N_{\mathrm{tours}}$	$t_{\mathrm{exp}}^{80\%}$ (min)	$t_{\mathrm{exp}}^{95\%}$ (min)	$N_{\mathrm{tours}}$	$t_{\mathrm{exp}}^{80\%}$ (min)	$t_{\mathrm{exp}}^{95\%}$ (min)	Exp@30 (%)	$N_{\mathrm{tours}}$
1	$\mathbf{8.12\pm 0.60}$	$\mathbf{14.13\pm 1.28}$	$8.0\pm 0.7$	$11.42\pm 1.19$	$18.53\pm 1.22$	$\mathbf{7.2\pm 0.4}$	$13.65\pm 1.95$	—	$92.8\pm 1.8$	$19.5\pm 1.5$
2	$\mathbf{9.97\pm 0.87}$	$\mathbf{15.46\pm 1.90}$	$9.2\pm 0.8$	$13.85\pm 0.99$	$24.18\pm 2.68$	$\mathbf{8.5\pm 0.5}$	$16.04\pm 2.28$	—	$90.3\pm 1.5$	$20.2\pm 0.8$
3	$\mathbf{5.23\pm 1.52}$	$\mathbf{8.63\pm 0.65}$	$\mathbf{5.0\pm 0.0}$	$5.89\pm 0.91$	$12.24\pm 1.33$	$5.2\pm 1.1$	$5.25\pm 1.32$	$10.86\pm 0.42$	—	$9.0\pm 1.0$

TABLE III : Comparison: Our Method vs. TERRA vs. UAV-only.

t_{\mathrm{exp}}^{80\%}

and

t_{\mathrm{exp}}^{95\%}

: active exploration time to reach 80% and 95% exploration.

N_{\mathrm{tours}}

: number of tours until termination. Exp@30 (%): the percentage of the environment explored by the 30-minute cutoff.

Fig. 7 : Experiment scenario. (a) A snapshot from the experiment during the first tour, (b) 2.5D Occupancy map built during the first tour of exploration, where light gray corresponds to occupied cells detected by UGV and dark gray corresponds to occupied cells detected by UAV.

Explore similar work

PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments
Apr 2, 2026Roger Fowler, Cahit Ikbal Er, Benjamin Johnsenberg +1Motion Planning Multi-Robot Coordination
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
Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment
Jul 7, 2026Md Safwan Mondal, Luca Russo, James D. Humann +2Vehicle Routing Multi-Robot Coordination
Department of Mechanical and Industrial Engineering, University of Illinois Chicago, IL, 60607 USA · DEVCOM Army Research Laboratory, Minneapolis, Minnesota, USA · DEVCOM Army Research Laboratory, Aberdeen Proving Grounds, Aberdeen, MD 21005 USA
Robust and Scalable UAV-UGV Planning for Aerial Monitoring under Energy and Precedence Constraints
Oct 8, 2026Cahit Ikbal Er, Yasin YaziciogluConstrained Motion Planning Vehicle Routing
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

Apr 2, 2026J/K move · Enter open · S save

PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

Roger Fowler, Cahit Ikbal Er, Benjamin Johnsenberg, Yasin Yazicioglu

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

Motion Planning Multi-Robot Coordination

Open paper AskPDF arXiv

We consider energy-aware planning for an unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) team operating in a stochastic environment. The UAV must visit a set of air points in minimum time while respecting energy constraints, relying on the UGV as a mobile charging station. Unlike prior work that assumed deterministic travel times or used fixed robustness margins, we model travel times as random variables and bound the probability of failure (energy depletion) across the entire mission to a user-specified risk level. We formulate the problem as a Mixed-Integer Program and propose PRO-SPECT, a polynomial-time algorithm that generates risk-bounded plans. The algorithm supports both offline planning and online re-planning, enabling the team to adapt to disturbances while preserving the risk bound. We provide theoretical results on solution feasibility and time complexity. We also demonstrate the performance of our method via numerical comparisons and simulations.

Abstract

Explore similar work

PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment

Robust and Scalable UAV-UGV Planning for Aerial Monitoring under Energy and Precedence Constraints