arxiv: 2604.22189 · v2 · submitted 2026-04-24 · 💻 cs.RO

Recognition: unknown

Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests

Sourav Raxit , Jose Fuentes , Paulo Padrao , Abdullah Al Redwan Newaz , Md Tamjidul Hoque , Mark Kulp , Leonardo Bobadilla

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:34 UTC · model grok-4.3

classification 💻 cs.RO

keywords multi-robot coverage path planningenergy-efficient path planningnon-convex regionsobstacles and no-fly zonesmTSP workload balancingvisibility graph connectionsaerial and surface vehicles

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The pith

The MRCPP framework reduces energy consumption by 3 to 40 percent and computation time by an order of magnitude for teams of robots covering non-convex regions with obstacles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the MRCPP framework for planning coverage paths for multiple robots over large non-convex areas that contain obstacles and no-fly zones. It improves existing methods by using globally informed swath generation to create parallel paths with minimal turns, adding safety buffers around turns, balancing robot workloads with an efficient mTSP solver, and linking path segments with a heading-aware visibility graph. A reader would care because coverage missions for monitoring or mapping often consume excess energy and require long planning times when robots must navigate complex shapes without colliding or violating restrictions.

Core claim

MRCPP achieves lower total energy use and faster planning for multi-robot coverage of non-convex ROIs by generating globally-informed swaths, producing parallel sweeping paths with minimal turns, inserting safety buffers for turning clearance, solving an mTSP instance to distribute workload evenly, and connecting disjoint segments through a modified visibility graph that preserves heading angles and stays inside safe regions.

What carries the argument

The five-step pipeline consisting of globally-informed swath generation, minimal-turn parallel sweeping, safety buffer calculation, mTSP workload balancing, and heading-aware visibility graph connections.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same pipeline could be adapted for coverage tasks performed by ground robots moving through cluttered indoor or urban spaces.
Reduced energy draw per mission could allow smaller batteries or longer operating times before recharging in field operations.
Testing the planner against environments where obstacles move would reveal whether the static safety buffers remain sufficient.
Releasing the code as open source lets other groups apply the method to new robot platforms and compare results directly.

Load-bearing premise

The specific mix of globally-informed swath generation, minimal-turn sweeping, safety buffers, mTSP balancing, and heading-aware connections will deliver the reported energy and time savings without hidden increases in path length or safety violations under varied real conditions.

What would settle it

Deploy the planner on a fresh non-convex ROI containing obstacles and no-fly zones, then measure that the robots' actual energy consumption or planning computation time shows no improvement over a standard boustrophedon-based method.

Figures

Figures reproduced from arXiv: 2604.22189 by Abdullah Al Redwan Newaz, Jose Fuentes, Leonardo Bobadilla, Mark Kulp, Md Tamjidul Hoque, Paulo Padrao, Sourav Raxit.

**Figure 1.** Figure 1: Real-world multi-robot coverage experiments. (a) Two AAVs cover view at source ↗

**Figure 2.** Figure 2: Illustration of the MRCPP-based multi-robot coverage workflow. Starting from an input workspace with an internal NFZ, the method computes a headland-adjusted ROI and a minimum-area enclosing rectangle to determine the sweep orientation, generates parallel sweep lines over the free space, and finally allocates the resulting swaths to three robots using mTSP. All intersections satisfying sµ ∈ [0, 1] are reta… view at source ↗

**Figure 3.** Figure 3: Representative benchmark environments with coverage paths for three AAVs in yellow, violet, and blue. The AAV paths are generated by our MRCPP view at source ↗

**Figure 5.** Figure 5: Effect of buffer scale w on the total fleet energy consumption Et across different environments. Lower values indicate better energy efficiency. TABLE IV PERFORMANCE COMPARISON OF MRCPP AND EAMCMP ACROSS VARYING FLEET SIZES IN THE PRESENCE OF 10 CONVEX AND NONCONVEX OBSTACLES. BOLD ENTRIES INDICATE THE BEST VALUE IN EACH METRIC PER FLEET CONFIGURATION. #Robots Method Eomax (Wh) E¯t (Wh) Tc (s) 3 MRCPP 41.8… view at source ↗

**Figure 4.** Figure 4: Coverage paths generated by (a) EAMCMP and (b) MRCPP for a view at source ↗

read the original abstract

This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing obstacles and no-fly zones (NFZ). Existing minimum-energy coverage planning algorithms utilize meta-heuristic boustrophedon workspace decomposition. Therefore, even with minimum energy objectives and energy consumption constraints, they cannot achieve optimal energy efficiency. Moreover, most existing frameworks support only a single type of robotic platform. MRCPP overcomes these limitations by: generating globally-informed swath generation, creating parallel sweeping paths with minimal turns, calculating safety buffers to ensure safe turning clearance, using an efficient mTSP solver to balance workloads and minimize mission time, and connecting disjoint segments via a modified visibility graph that tracks heading angles while maintaining transitions within safe regions. The efficacy of the proposed MRCPP framework is demonstrated through real-world experiments involving autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs). Evaluations demonstrate that the proposed MRCPP consistently outperforms state-of-the-art planners, reducing average total energy consumption by 3\% to 40\% for a team of 3 robots and computation time by an order of magnitude, while maintaining balanced workload distribution and strong scalability across increasing fleet sizes. The MRCPP framework is released as an open-source package and videos of real-world and simulated experiments are available at https://mrc-pp.github.io.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This MRCPP planner adds a practical pipeline of swath generation, turn-minimizing sweeps, safety buffers, mTSP balancing, and heading-aware graphs for multi-robot coverage in non-convex areas with NFZs, but the 3-40% energy savings rest on unstated details of the energy model and baseline re-implementations.

read the letter

This paper's main contribution is a multi-robot coverage planner for non-convex regions that integrates globally-informed swath generation, minimal-turn parallel sweeps, safety buffers, mTSP workload balancing, and a heading-aware visibility graph for transitions. It shows real-world results on AAVs and ASVs with claimed energy and time improvements. The new elements are the tailored handling of non-convex ROIs and NFZs in a single framework that supports multiple robot platforms, going beyond standard boustrophedon extensions. Releasing the code and providing experiment videos helps others use and verify it. The scalability tests with increasing fleet sizes and the focus on balanced distribution are solid additions for practical use. The work does well in presenting an end-to-end solution with experiments on actual hardware, which is better than many planning papers that stay in simulation. The soft spots center on the energy efficiency claims. The reported 3% to 40% reduction in total energy for three robots and order-of-magnitude faster planning depend on the specific energy model and how baselines were set up. The abstract does not include the energy function details or explicit confirmation of identical baseline conditions, so the savings could partly stem from modeling choices rather than the algorithm alone. This is the issue flagged in the stress-test note, and it is worth checking in the full paper. The rest of the approach looks sound, with no obvious circularity or invented entities. This is for applied robotics people working on coverage path planning for teams of robots in irregular areas. Readers who need a ready framework for energy-aware multi-robot tasks in real settings will get the most out of it. It deserves a serious referee. The experimental component and open implementation make it worth the time to review and potentially improve. I would recommend accepting it for peer review.

Referee Report

3 major / 3 minor

Summary. The paper proposes the MRCPP framework for energy-efficient multi-robot coverage path planning (CPP) in large non-convex regions of interest (ROIs) containing obstacles and no-fly zones (NFZs). It combines globally-informed swath generation, parallel sweeping with minimal turns, safety buffers for turning clearance, an mTSP solver for workload balancing, and a modified heading-aware visibility graph for connecting segments. The framework is evaluated in real-world experiments with autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs), claiming consistent outperformance over state-of-the-art planners with 3-40% lower average total energy consumption for teams of 3 robots, an order-of-magnitude reduction in computation time, balanced workload, and good scalability with fleet size. The code is released as open-source.

Significance. If the energy savings and computational gains hold under consistent modeling and fair baselines, the work would offer a practical advance for multi-robot coverage in complex environments, with direct relevance to applications such as environmental monitoring or search. The combination of geometric decomposition with optimization-based balancing and the real-world validation on two distinct platforms (AAV/ASV) are strengths; the open-source release further increases potential impact.

major comments (3)

[Methodology (swath generation and path connection subsections)] The energy consumption model is never stated as an equation or explicit function anywhere in the method description. Without a precise definition (e.g., whether it includes only path length, turning-radius costs, acceleration profiles for AAVs, or hydrodynamic drag for ASVs), it is impossible to verify that the reported 3-40% energy reduction is produced by the algorithmic choices rather than by differences in how energy is scored for MRCPP versus the re-implemented baselines.
[Experiments and Results] The experimental section provides no description of how the state-of-the-art baselines were re-implemented under identical constraints (non-convex ROI boundaries, obstacle polygons, and NFZ polygons). If the baselines were allowed simpler convex decompositions or different safety margins, the claimed energy and runtime advantages cannot be attributed solely to the proposed pipeline of globally-informed swaths + mTSP + heading-aware visibility graph.
[Experiments and Results] No statistical analysis (standard deviations, number of trials, or significance tests) accompanies the energy and time figures. The abstract states “average total energy consumption” reductions without reporting variance or the exact number of independent runs, which is required to support the central performance claim.

minor comments (3)

[Abstract] The abstract lists five algorithmic components but does not name the specific SOTA planners used for comparison; adding the names (e.g., “compared against Boustrophedon decomposition of X et al. and Y et al.”) would improve clarity.
[Figures] Figure captions for the real-world AAV and ASV trajectories should explicitly state the ROI area, number of obstacles/NFZs, and the exact energy model used to color the paths.
[Workload balancing subsection] The mTSP formulation is referenced but the objective function (minimize makespan or total distance?) and the solver implementation details are omitted; a short paragraph or pseudocode would help reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our MRCPP framework. We address each major comment point by point below, providing clarifications and committing to revisions that improve the manuscript's transparency, reproducibility, and statistical rigor without altering the core contributions.

read point-by-point responses

Referee: [Methodology (swath generation and path connection subsections)] The energy consumption model is never stated as an equation or explicit function anywhere in the method description. Without a precise definition (e.g., whether it includes only path length, turning-radius costs, acceleration profiles for AAVs, or hydrodynamic drag for ASVs), it is impossible to verify that the reported 3-40% energy reduction is produced by the algorithmic choices rather than by differences in how energy is scored for MRCPP versus the re-implemented baselines.

Authors: We agree that an explicit equation would strengthen verifiability. The energy model components (path length, minimum-turning-radius penalties for AAVs, and hydrodynamic drag terms for ASVs) are described in the experimental setup and applied uniformly, but were not formalized as a single equation in the methodology. In the revised manuscript we will add a dedicated equation in the method section defining the total energy function E(·) with all terms, platform-specific parameters, and confirmation that the identical model is used for MRCPP and all baselines. revision: yes
Referee: [Experiments and Results] The experimental section provides no description of how the state-of-the-art baselines were re-implemented under identical constraints (non-convex ROI boundaries, obstacle polygons, and NFZ polygons). If the baselines were allowed simpler convex decompositions or different safety margins, the claimed energy and runtime advantages cannot be attributed solely to the proposed pipeline of globally-informed swaths + mTSP + heading-aware visibility graph.

Authors: We acknowledge the need for explicit implementation details to support fair comparison. The baselines were adapted to the same non-convex polygon inputs, obstacle/NFZ representations, and safety-buffer rules as MRCPP. To address the gap, the revised experimental section will include a dedicated paragraph (and possibly a table) describing the precise modifications made to each baseline so that all methods operate under identical environmental constraints. revision: yes
Referee: [Experiments and Results] No statistical analysis (standard deviations, number of trials, or significance tests) accompanies the energy and time figures. The abstract states “average total energy consumption” reductions without reporting variance or the exact number of independent runs, which is required to support the central performance claim.

Authors: This is a fair observation on statistical reporting. The reported averages derive from repeated trials, but variance, trial counts, and significance tests were omitted. In the revision we will augment all energy and runtime tables/figures with standard deviations, state the exact number of independent runs (simulation and real-world), and add appropriate statistical tests to substantiate the performance claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical comparisons to external SOTA baselines

full rationale

The paper introduces an algorithmic MRCPP framework via a pipeline of swath generation, minimal-turn sweeping, safety buffers, mTSP balancing, and heading-aware visibility graphs. Performance is evaluated through real-world AAV/ASV experiments showing 3-40% energy reduction and faster computation versus state-of-the-art planners. No equations, fitted parameters presented as predictions, or self-citation chains appear in the abstract or described method; the central claims are externally benchmarked rather than internally self-referential. The derivation chain is self-contained against external comparisons.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach builds on standard computational geometry and optimization techniques without introducing new physical entities or many fitted parameters beyond algorithmic tuning.

axioms (2)

domain assumption The environment map with obstacles and no-fly zones is known a priori
Required for generating swaths and paths in the described framework.
domain assumption Robots have standard kinematic models allowing for turning with safety buffers
Implicit in the calculation of safety buffers for turning clearance.

pith-pipeline@v0.9.0 · 5570 in / 1417 out tokens · 64606 ms · 2026-05-08T11:34:43.899723+00:00 · methodology

discussion (0)

Reference graph

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