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Energy-Aware Collaborative Exploration for a UAV-UGV Team

Cahit Ikbal Er, Saikiran Juttu, Yasin Yazicioglu

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.

Energy-Aware Collaborative Exploration for a UAV-UGV Team

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.
Paper Structure (14 sections, 2 equations, 8 figures, 3 tables, 3 algorithms)

This paper contains 14 sections, 2 equations, 8 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Proposed Method
  4. Building the PRM
  5. Orienteering Problem
  6. UAV-UGV Coordination
  7. Exploration Pipeline
  8. Simulations
  9. Setup
  10. Results
  11. Benchmark with Single-UAV
  12. Benchmark with UAV-UGV Team
  13. Experiments
  14. Conclusion

Figures (8)

  • Figure 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).
  • Figure 2: Consecutive exploration tours of the UAV and the UGV.
  • Figure 3: Environments used in the simulations.
  • Figure 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.
  • Figure 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
  • ...and 3 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2: Feasibility and Exploration Progress