RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring

TL;DR

The paper tackles robust, energy-aware coordination of UAV-UGV teams for long-horizon aerial monitoring by formulating a challenging mixed-integer problem and introducing RSPECT, a scalable heuristic that treats the task as a GTSP/mTSP-inspired planning problem. RSPECT partitions UAV targets among teams, constructs per-team tour plans via TSP, and selects optimal collect points through GTSP, with a formal robustness analysis ensuring feasibility under disturbances. The authors prove algorithmic complexity and robustness guarantees, and validate performance through extensive simulations and real-world experiments, showing favorable scalability and superiority over Branch-and-Cut and several heuristics. The work offers practical improvements for deploying energy-constrained multi-robot monitoring missions with mobile charging infrastructure and robust offline planning that tolerates uncertainty.

Abstract

We consider the robust planning of energy-constrained unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which act as mobile charging stations, to perform long-horizon aerial monitoring missions. More specifically, given a set of points to be visited by the UAVs and desired final positions of the UAV-UGV teams, the objective is to find a robust plan (the vehicle trajectories) that can be realized without a major revision in the face of uncertainty (e.g., unknown obstacles/terrain, wind) to complete this mission in minimum time. We provide a formal description of this problem as a mixed-integer program (MIP), which is NP-hard. Since exact solution methods are computationally intractable for such problems, we propose RSPECT, a scalable and efficient heuristic. We provide theoretical results on the complexity of our algorithm and the feasibility and robustness of resulting plans. We also demonstrate the performance of our method via simulations and experiments.

Figures (8)

• Figure 1: Each team (UAV-UGV pair) has given start/end positions. The goal is to achieve robust offline planning of the UAV-UGV teams to minimize the mission completion time while ensuring all aerial monitoring points are visited by one of the UAVs without violating the UAVs' energy constraints, even under disturbances that cause longer travel times or deviations from the plan.
• Figure 2: Output of Alg.\ref{['alg:integrated_path_planning']} for $m=4$ teams (Top View). Left: $\mathcal{P}_{\text{UAV}}$ (gray points). In Step 1, assigned points to teams are color-coded with upward/downward triangles showing $p_{\text{o}}^{\mu}$/$p_{\text{f}}^{\mu}$. Steps 2.1--2.3 show the detailed planning for one of the teams (blue, $\mu=1$). In Step 2.1, the path shows the initial tour over $\mathcal{P}^{\mu}_{\text{UAV}}$ (blue points from Step 1). Upward and downward blue triangles are the initial and final positions, respectively. In Step 2.2, blue lines are feasible tours for the UAV-UGV team, and hollow pentagons are feasible collect points for each tour. In Step 2.3, the plan is obtained, filled pentagons represent the selected collect point for that tour, and dashed grey lines are the UGV path.
• Figure 3: Illustration for Remark \ref{['remark']}. A tour with its original and modified release/collect points due to unknown obstacles. (a) top view, (b) 3D view.
• Figure 4: Comparing overall mission time ($\tau(X^{1})$) of Alg.\ref{['alg:integrated_path_planning']} and TERRA ropero2019terra for different problem sizes ($n$) and recharge ratio ($\gamma$).
• Figure 5: Comparing computation time of Alg.\ref{['alg:integrated_path_planning']} and TERRA ropero2019terra for different problem sizes ($n$) and recharge ratio ($\gamma$).

Theorems & Definitions (8)

• Definition 1
• Theorem 1
• proof
• Theorem 2
• proof
• Corollary 1
• proof
• Remark 1