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

arXiv:2511.21957 · cs.RO, cs.MA · Submitted 2025-11-26 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: I'm Rosa, and with me are Dev and Taro, guest researcher.

Dev: Today's paper: "RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring".

Rosa: The gist The proposed RSPECT algorithm provides a scalable and efficient heuristic for robust planning of energy-constrained UAV-UGV teams to perform long-horizon aerial monitoring missions,

Dev: First, who's behind it and why it matters.

Title and authors: Rosa: We just talked about what RSPECT is trying to achieve—robust planning for energy-constrained UAV-UGV teams to do long-horizon monitoring. Now, let's talk about the authors and what their title really suggests.

Dev: The paper is written by Cahit Ikbal Er, Amin Kashiri, and Yasin Yazıcıo˘glu. They come from both the Mechanical and Industrial Engineering department at Northeastern University, which gives them a solid background in the physical systems involved.

Taro: What's interesting is that they aren't just looking at one type of coordination; they are specifically targeting the challenge of uncertainty—things like unknown obstacles or unpredictable wind—which is what makes these missions risky in practice.

Rosa: Right, and they frame it as a problem that needs a robust plan, meaning the trajectories have to be executable without major revisions when you hit those uncertainties. It's not just about finding *a* plan; it's about finding one that stays good even when the world throws curveballs at it.

Dev: The title itself, "Robust and Scalable Planner," tells us they are tackling two big things: robustness against uncertainty, and scalability so the method can handle a lot of points or many teams without getting completely bogged down in computation.

Taro: I think that scalability is crucial here because if we have a huge area to monitor, the number of points gets massive, and any planning method that takes too long just doesn't work for operational use.

Rosa: So, they are aiming for something practical—a planner that works well enough in the real world where things are messy—and they introduce RSPECT as their tool for this job. This sets the stage for how we’re going to look at their actual approach next.

The paper's summary: Dev: Moving on to the summary of "RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring," the main idea is that they tackle the long-horizon monitoring problem by formulating it as a mixed-integer program, which we know is NP-hard.

Rosa: They then propose RSPECT as this scalable heuristic. It breaks the problem down into two main steps: first, partitioning all those points among different teams and figuring out an efficient sequence for each team using a Nearest Neighbor TSP heuristic.

Taro: That sounds like they’re trying to tackle the assignment problem first—who does which part of the monitoring—and then optimizing the path within that assignment. How do they handle the energy constraints during that sequencing?

Dev: In Step one they assign each point to a team, and they pick the team whose addition minimizes the resulting maximum path cost across all teams <ref:2511.21957#pg1>. That’s a way of balancing the load upfront before actually drawing any flight paths.

Rosa: Then in Step two which is where things get more detailed, they generate a feasible plan based on that sequence <ref:2511.21957#pg1>. They take that sequence and break it into maximal tours that are feasible for both the UAV and the UGV simultaneously.

Taro: I’m curious about how they handle the actual path generation there; is it just a standard route, or is there something more sophisticated because we have to worry about energy?

Dev: They use a Generalized Traveling Salesperson Problem, or GTSP, in Step two point two to select the best "collect point" for each tour <ref:2511.21957#pg1>. They solve that GTSP using the GLNS heuristic with a runtime bound of sigma cubed mu, which keeps the calculation manageable even with many teams and points.

Rosa: So they’re essentially trying to find a sequence that balances workload efficiently, and then they use a known approximation method for path selection within those sequences to make it work quickly. This is their main contribution to making this NP-hard problem tractable for real applications.

The paper's improvements: Taro: If we look at the improvements they suggest in "RSPECT," they are really focused on adding formal guarantees around robustness, which is a huge step since most previous work just assumed perfect knowledge of the environment.

Dev: They introduce Theorem one which gives them a worst-case time complexity of O(mn + n3), meaning we have a solid theoretical limit on how long the algorithm will take as the number of teams and points grows <ref:2511.21957#pg1>.

Rosa: Beyond just speed, they provide Theorem two and Corollary one for robustness <ref:2511.21957#pg1>. These theorems give us sufficient conditions under which their generated plan can actually be executed even when there are unknown obstacles or wind disturbances.

Taro: What does that actually mean for me on the ground? It means if you have a slight disturbance, you don't need to stop everything and re-plan instantly; you just need to make small adjustments to where the vehicles release or collect themselves.

Dev: Corollary one gives a practical strategy for this online modification <ref:2511.21957#pg1>. It suggests that points within a certain radius, depending on parameters like delta a and delta g, can be used as modified release and collect points for the teams. That’s how you adapt without doing a full re-run of the expensive planning algorithm.

Rosa: And they tune these robustness margins by adjusting those specific parameters, delta a and delta g. The paper shows that you can trade off how robust your plan is against how much extra mission time it takes to keep things safe or robust.

Conclusion: Dev: So, to wrap up on "RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring," the paper shows a way to tackle this very difficult energy-aware coordination problem using a two-step heuristic approach.

Rosa: The core idea is partitioning the points first, and then using an efficient GTSP solver to pick the best collection points for each team's tour sequence. They manage to achieve a worst-case complexity of O(mn + n3), which is quite good when compared against exact methods like Branch and Cut.

Taro: I think what really stands out is the formal robustness analysis they provide, showing that with specific conditions on how much things can deviate, the plan remains feasible under disturbances. That moves it beyond just being a clever approximation to being a provably safer planning tool.

Dev: And the online adaptation suggested by Corollary one means that in a real operational setting, if you encounter an unknown obstacle, you don't have to halt and recalculate everything; you can just tweak the release and collect points based on those calculated robustness margins <ref:2511.21957#pg1>.

Rosa: Overall, "RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring" gives us a scalable way to plan these complex monitoring missions while providing theoretical guarantees about how well that plan holds up against real-world uncertainties.

Taro: It’s a solid piece of work because it bridges the gap between having a mathematically sound planning problem and actually finding an efficient heuristic to solve it in time for deployment.

Dev: We've covered the structure, the efficiency, and how they handle uncertainty with their robustness guarantees in this paper.

Rosa: That’s all we have for today on RSPECT.

Department of Mechanical and Industrial Engineering, Northeastern University · Department of Electrical and Computer Engineering, Northeastern University

cs.RO, cs.MA

Submitted: 2025-11-26

Updated: 2026-06-14

Comments: Accepted to the Journal of Intelligent & Robotic Systems (JINT)

Journal ref: J Intell Robot Syst 112, 91 (2026)

DOI: 10.1007/s10846-026-02426-5

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 79/100

The gist: The gist The proposed RSPECT algorithm provides a scalable and efficient heuristic for robust planning of energy-constrained UAV-UGV teams to perform long-horizon aerial monitoring missions, offering

Key concepts

Robust Planning: This involves creating vehicle trajectories that can successfully complete the mission even when faced with uncertainties like unknown obstacles or wind. The goal is to find a plan that remains viable without requiring major revisions when disturbances occur.
RSPECT Algorithm: RSPECT is a heuristic designed to solve the complex, NP-hard robust planning problem efficiently. It works in two main steps: first, partitioning monitoring points among teams using a Nearest Neighbor TSP approach; second, generating feasible plans for each team by breaking sequences into tours and selecting optimal collection points via a Generalized TSP.
Energy Constraint: The UAVs have limited energy, which dictates how long they can fly between charging stations (UGVs). The planning process must explicitly account for this constraint by including recharge times in the total mission time calculation to ensure feasibility.
Robustness Guarantees: The paper provides theoretical conditions showing that the generated plan can handle unknown obstacles by simply adjusting release and collect points within certain bounds. This ensures all monitoring points are visited while respecting energy limits under bounded disturbances.

Terminology

Robustness — 1× in this paper · explained in 5 episodes

Summary

The gist The proposed RSPECT algorithm provides a scalable and efficient heuristic for robust planning of energy-constrained UAV-UGV teams to perform long-horizon aerial monitoring missions, offering formal robustness guarantees that have not been previously achieved for this type of problem

Problem Formulation

The core problem involves finding a robust plan (the vehicle trajectories) for energy-constrained unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which act as mobile charging stations, to perform long-horizon aerial monitoring missions given a set of points to be visited by the UAVs and desired final positions of the UAV-UGV teams The overall mission goal is for the teams to collectively visit a set of n points in the air, PUAV, while minimizing completion time under energy constraints and bounded disturbances This is formulated as a mixed-integer program (MIP), which is NP-hard The mission plan of each team µ ∈ m is encoded as a matrix Xµ, where each row i of Xµ is used to encode a tour The total time to execute the respective plan Xµ is defined by equation (2), which accounts for UAV traversal time, UGV travel time between tours, and recharge time

Proposed Solution (RSPECT Algorithm)

RSPECT is proposed as a scalable and efficient heuristic to solve the NP-hard planning problem The algorithm consists of two main steps, illustrated in Fig. 2 Step 1 partitions PUAV among the m teams and constructs an efficient visit sequence for each team using a NearestNeighbor TSP heuristic (NN-TSP) The point p is then assigned to the team µ⋆ whose addition minimizes the resulting maximum path cost across all teams Step 2 generates a feasible plan for each team based on its sequence Sµ from Step 1 In Step 2.1, a feasible plan Xµ is created by breaking the sequence Sµ into maximal tours that are feasible both for the UAV and the UGV The collect point of the current tour is set as the release point, serving as a valid default structure In Step 2.2, a feasible plan Xµ is constructed using a Generalized Traveling Salesperson Problem (GTSP) to select the best collect point for each tour The GTSP is solved using the GLNS heuristic with a bounded runtime of σn3µ, where σ is a user-defined constant

Complexity and Robustness Guarantees

The worst-case time complexity of Alg. 1 is O(mn + n3), where m is the number of UAV-UGV teams and n = PUAV is the number of monitoring points The total runtime of Step 1 is O(mn + n3), and Step 2 has a worst-case complexity of O(n3) Theorem 1 proves that Alg. 1 returns a feasible solution to (3) if one exists, and it has the worst-case time complexity of O(mn + n3) The algorithm provides theoretical results on the computational complexity (Theorem 1) and robustness (Theorem 2, Corollary 1)

Robustness Analysis

Theorem 2 states sufficient conditions under which the plan generated by Alg. 1 can be executed in the face of unknown obstacles by simply adjusting the release and collect points This requires that for each tour i, τ˜a(X˜µi) ≤ τa(Xµi) + ˆδµ,ia(X), and τ˜g(X˜µi,1, X˜µi,n+2) ≤ τg(Xµi,1, Xµi,n+2) + ˆδµ,ig(X) These conditions ensure that every point in PUAV is visited while respecting the energy limitation of the UAVs under the modified plan X˜ Corollary 1 provides a practical strategy for online modification, suggesting that feasible points within a certain radius, depending on ˆδµ,ia(X), can be used as modified release/collect points

Performance and Comparison

Simulations demonstrate that Alg. 1 scales well across different m and n configurations The computation time increases with n as expected from the O(mn + n3) complexity established in Theorem 1, with empirical scaling exponents consistently well within the theoretical cubic bound Alg. 1 completes in under 0.10 s across all tested configurations Comparisons against Branch and Cut show that Alg. 1 completes in under 0.03 s for m=4, n=5, while Branch and Cut takes up to 3830 ± 470 s Furthermore, the performance of Alg. 1 is competitive with near-optimal Branch and Cut reference solutions The sensitivity analysis shows that substantial robustness margins can be achieved at a small cost in mission efficiency, particularly when multiple teams are available to share the workload

Ablation Studies

The ablation study confirms that the explicit feasibility construction in Step 2.1 is responsible for the dominant share of mission time relative to a non-energy-aware tour The GTSP-based selection in Step 2.2 consistently finds collect-point combinations that lie below the mean of mission times obtainable from valid choices in Cµi The results show that the energy-feasibility cost ∆feas is large (around 150-340% across all (m, n)), confirming that the explicit feasibility construction in Step 2.1 is responsible for the dominant share of mission time relative to a non-energy-aware tour The ∆GTSP gap is consistently negative across all (m, n) tested (3-11%), indicating that the GTSP-based selection in Step 2.2 consistently finds collect-point combinations that lie below the mean of mission times obtainable from valid choices in Cµi The algorithm explicitly leverages the structure of (3), achieving consistently lower maxµ τ(Xµ) with negligible computation time

Experimental Validation

Experiments with two teams demonstrated the applicability of the proposed approach in a multi-team setting The realized tour robustness values ˆδµ,ia(X) and ˆδµ,ig(X) illustrate the role of the design parameters δa and δg The experiment video is available in the supplementary materials and online The results confirm that Alg.

Improvements for AI systems

Robust Multi-Agent Planning for Monitoring Missions: The RSPECT heuristic can generate a scalable and efficient heuristic to solve NP-hard problems, enabling UAV-UGV teams to perform robust planning of UAV-UGV teams to perform long-horizon aerial monitoring missions.
Formal Robustness Guarantees in Planning: The algorithm provides theoretical results on the computational complexity (Theorem 1) and robustness (Theorem 2, Corollary 1), ensuring that the plan is feasible even in uncertain environments by enforcing constraints like (3c) and (3d) which ensure the UAVs’ energy constraints are met under bounded disturbances.
Efficient Heuristic for Large-Scale Coordination: RSPECT employs a polynomial-time and efficient heuristic, RSPECT (Alg.1), to solve this NP-hard planning problem, achieving a worst-case time complexity of O(mn + n3), which is significantly faster than exact methods like Branch & Cut for large instances.
Adaptive Robustness Tuning: The system allows for the optimization of robustness margins by tuning parameters δa and δg, enabling a trade-off where Selecting larger values of δa and δg leads to a plan X with more robust tours, but it also reduces the feasible space of the planning problem, potentially leading to solutions with higher cost mission times.
Dynamic Online Adaptation for Unknown Obstacles: When facing unknown obstacles, Corollary 1 allows for online modification of the release and collect points, where the deviation is bounded by v avg H · ˆδ µ,i a(X) and ground path length increases are limited by v avg g · ˆδ µ,i g(X).