Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles

Chao Xu; Fei Gao; Ruibin Zhang; Tiancheng Lai; Yanjun Cao; Yuman Gao

REVIEW 4 major objections 5 minor 29 references

Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims a fly-and-roll robot can choose its mode leg by leg so as to maximize explored information while still returning home within energy and time budgets, and supports the claim with simulations and a real deployment.

desk verdict BM-MCTS is a genuine new module for modality-aware exploration, but the simulation's resource metrics are generated by the same constant-power model the planner optimizes, so independent validation is needed. read the letter →

arxiv 2507.21338 v1 pith:2YKQON5B submitted 2025-07-28 cs.RO

Yuman Gao , Ruibin Zhang , Tiancheng Lai , Yanjun Cao , Chao Xu , Fei Gao This is my paper · ORCID

classification cs.RO

keywords autonomousexplorationterrestrial-aerialbimodalvehicleMonteCarlotreesearchenergy-awareplanningtime-awareviewpointmotionfrontier-based

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that a single robot that can both roll and fly can explore an unknown 3-D space under explicit energy and time budgets, if the choice of locomotion mode is made part of the planning problem rather than fixed by hardware. The authors build a hierarchical planner that generates candidate aerial and hybrid viewpoints, uses an extended Monte Carlo tree search to choose both the order of viewpoints and the modality used to reach each one, and sends the chosen path to a bimodal motion planner. They formulate the task as maximizing information gain subject to nonnegative remaining energy and time, with a harder penalty on running out of energy than on running late. In simulation the bimodal planner outperforms an aerial-only baseline under energy shortage, reaching 93.2 percent coverage versus 58.4 percent in the benchmark case, and stays comparable under time shortage. A real platform completes an underground-garage exploration on a 20 Wh budget, so the payoff, if the claim holds, is that practical search-and-rescue exploration can be done by one vehicle instead of a coordinated aerial-ground team.

What carries the argument

The central object is BM-MCTS, a Monte Carlo tree search extended from the classic selection-expansion-simulation-backpropagation loop. Each tree node is a viewpoint; each root-to-leaf branch is a candidate traverse sequence; the modality used to travel into a node is encoded by that node, so the search decides modality and sequencing at once. The reward per node splits into a process gain, a discounted average of visible frontier-voxel information, and a terminal cost built from exponential penalties on remaining energy and time, and the UCB rule balances exploration against exploitation when choosing children. A grouped TSP over frontier-cluster viewpoints, evaluated on a topological graph with an average-modality cost model, supplies the long-horizon estimate of what it takes to finish and return. The energy model behind the estimates is constant power per modality, $E(P_i,P_j,M)=P_M T(P_i,P_j,M)$, with flying power roughly seven times rolling power.

What would settle it

Measure the physical platform's battery draw across repeated stair ascents, edge crossings, and flat rolling in both modalities and compare integrated energy use with $E(P_i,P_j,M)=P_M T(P_i,P_j,M)$. If the actual energy per meter is not approximately constant per modality, or the aerial-to-terrestrial ratio departs materially from 7 on these maneuvers, then the planner's simulated remaining-energy estimates, and the modality switches they cause, would not carry over to hardware.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that resource-constrained exploration of a bimodal vehicle reduces to a search over viewpoint sequences in which every edge of the sequence carries a modality label, and that this joint search is tractable enough to run online. The BM-MCTS tree stores discounted information gain and exponential penalties for low remaining energy and time; a simulation phase estimates the cost of finishing all frontier clusters and returning home by solving a grouped traveling-salesman path on a topological graph, and a pruning rule stops expanding any branch that cannot return with nonnegative energy. The same framework is then coupled to a motion planner that perceives terrain online, builds separate maps for rolling and flying, and applies modality-specific dynamics. The reported consequence is that the robot's mode choices follow the budget: it flies while energy is plentiful and information is high, rolls when energy is scarce, and switches early enough to leave a safety margin for coming back.

Load-bearing premise

The plan's value rests on the assumption that a flying robot uses roughly seven times the power of the same robot rolling, at a constant per-second rate, for every maneuver including stairs and terrain edges; if real power draw varies with terrain or speed, the modality choices are optimized for a model rather than for the vehicle.

Editorial extensions

If this is right

A single terrestrial-aerial vehicle can replace a multi-robot air-ground team for budgeted exploration, avoiding multi-robot SLAM, communication, and coordination overhead.
Energy-limited missions can be planned to prioritize returning with data: the planner stops expanding branches whose remaining energy cannot cover the trip home.
The modality ratio responds to the budget: tighter energy budgets push the vehicle toward rolling, tighter time budgets push it toward flying, and the planned margin leaves room for uncertainty.
Computational cost stays manageable as the number of frontier clusters grows, because the topo-graph makes the simulation-phase path estimates cheap.
The framework works with limited field-of-view sensors by passing the sensor FoV into viewpoint generation, so the same planner can be fitted to different perception hardware.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same BM-MCTS scheduling could be transferred to other hybrid mobility platforms, for example legged or hopping vehicles with two very different cost regimes, as long as the per-modality cost model is recalibrated; the paper does not test this transfer.
The steep energy penalty versus mild time penalty encodes a particular mission priority: returning alive matters more than finishing on time. Swapping those priorities would change the branches the tree prunes, so the reported behaviors are partly a consequence of that design choice rather than of the search alone.
A natural follow-up is to replace the constant-power energy model with terrain-aware consumption learned from onboard current sensing; the paper's own limitation section notes that resource estimates are locally myopic, and a terrain-sensitive model would address both that and the constant-power assumption.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

BM-MCTS is the real new piece: it folds locomotion modality into the MCTS decision under energy and time budgets, and the paper builds a complete exploration system around it, with a hardware demo. That is worth taking seriously. The main soft spot is that the simulation's resource metrics come from the same constant-power model the planner optimizes, so the balancing results are partly self-confirming.

The paper does several things well. The two coverage strategies (aerial-only vs hybrid) for viewpoint generation are a sensible way to give the planner modality options. The supplementary comparisons are stronger than the main text suggests: they include FUEL as a framework baseline, UAV-only/UGV-only/UGV-cross ablations, and NBV/TSP algorithm comparisons. The UGV-cross ablation is the right control and shows the MCTS's proactive switching buys something over a rule-based 'fly only when necessary' policy. The real-world run, though single, is a genuine demonstration with a 7.2x power ratio and a hard energy budget.

Soft spots, in order. First, Eq. (4) is a constant-power model, and Eq. (7) charges a whole segment at the arrival viewpoint's modality, so a terrestrial segment that actually needs a brief flight for a stair or edge is billed at terrestrial power. Second, the simulation's remaining energy and time are computed with the same model, so Fig. 8's budget-adaptation trends are partially circular. Third, the hardware experiment is one run, no baseline, and no predicted-vs-measured segment comparison, so it does not independently validate the model. These are real limitations, but they don't sink the contribution; the system works end-to-end at least once, and the ablations are consistent with the model. The authors also acknowledge simplified terrain analysis and local resource estimation in the supplementary limitations.

Who this is for: anyone working on energy-aware exploration or multi-modal robots will get something from the MCTS formulation and the ablation design. It deserves a serious referee. I'd send it out, and in review I'd push for independent battery logging, repeated runs, error bars, and a clear separation between model-generated metrics and measured ones. With that added, the conditional becomes a solid accept.

Referee Report

4 major / 5 minor

Summary. The paper proposes a hierarchical autonomous exploration framework for terrestrial-aerial bimodal vehicles (TABVs). The system generates bimodal viewpoints for frontier clusters, uses an extended Monte Carlo Tree Search (BM-MCTS) to select viewpoint sequences and locomotion modalities under energy and time budgets, and integrates an improved bimodal motion planner with terrain perception. The claimed contribution is that the vehicle adapts its modality to satisfy E_all and T_all constraints, achieving higher coverage than aerial-only baselines under energy shortage and comparable performance under time shortage. The paper reports simulations in multi-story buildings, budget-adaptability experiments, and a single real-world deployment in an underground parking garage.

Significance. If the claims hold, the work is a valuable step toward practical deployment of TABVs for search-and-rescue-style exploration, where energy and time budgets matter. The system is complete: perception, viewpoint generation, decision making, motion planning, and control are integrated, with a real-world demonstration. The supplementary material provides useful ablations: comparisons against FUEL, UAV-only, UGV-only, and UGV-cross baselines, and an analysis of the topo-graph's computational advantage. The problem formulation in Eq. (1)-(2) is clear, and the BM-MCTS design with discounted information gain and exponential penalties is well motivated. However, the evaluation has important weaknesses: the simulation energy/time metrics are generated by the same constant-power model the planner optimizes, the real-world test has no baseline, and several key comparisons appear only in the supplementary rather than the main text.

major comments (4)

[Sec. V-A, Eq. (4); Sec. VII-B] The constant-power energy model E(P_i, P_j, M) = P_M * T(P_i, P_j, M) is load-bearing: the remaining-energy and remaining-time values reported in Sec. VII-A and in the supplementary comparisons (Tabs. 1-2) are computed from the same model that BM-MCTS optimizes. The model ignores state-dependent power draw, such as stair climbing, edge transitions, and terrain resistance, even though the motion planner explicitly uses aerial primitives for such cases (supplementary Sec. 2, Fig. 4). The real-world experiment in Sec. VII-B reports a single run without a baseline, without independent battery measurements, and without comparing predicted versus measured segment consumption. As a result, the simulated modality-balancing behavior may be an artifact of the cost model rather than physical reality. I request either (a) independent validation of Eq. (4) against logged power data from the platform, or (b) repeated real-world runs comparing the full system against UAV-only and UGV-only baselines, with measured battery consumption.
[Sec. V-B, Eq. (7)] Equation (7) charges the entire segment from P_i to P_{i+1} at the power of the modality assigned to the arrival viewpoint, M(P_{i+1}). This means a terrestrial-to-terrestrial segment that actually requires a brief flight (e.g., to climb stairs, as illustrated in Fig. 4 of the supplementary) is charged at terrestrial power, underestimating energy consumption. This bias could systematically favor terrestrial choices in simulation, affecting the reported coverage and remaining-energy values. Please either modify the cost model to account for intermediate modality switches, or provide evidence that such mixed-modality segments are rare enough in the tested scenes to be negligible.
[Sec. VII-A, Fig. 8; supplementary Tabs. 1-2] The paper reports five or ten trials per condition but does not provide error bars, standard deviations, or statistical significance for the coverage percentages and remaining-energy/time values. For example, in supplementary Tab. 2, Case 1, the coverage difference between UGV-cross (92.1%) and Ours (93.2%) is small, and without variance information it is unclear whether this difference is meaningful. Please report the variance across trials and, where appropriate, statistical tests for the key comparative claims.
[Sec. V-C, Eq. (8); supplementary Sec. 5] The guidance path in the simulation phase uses the average modality M = (T+A)/2 (Sec. V-C and Eq. (8)), whereas the tree expansion uses actual per-segment modalities (Eq. (7)). The supplementary (Sec. 5) acknowledges this inaccuracy and argues that the UCB selection policy progressively corrects it, but no empirical evidence is given that the correction converges within the iteration counts used in Sec. VII-A (e.g., ten iterations in supplementary Sec. 8.1). Please quantify the estimation error introduced by the average-modality assumption, for example by comparing the guidance-path estimates against the fully expanded tree costs, or by analyzing sensitivity to the choice of P_{(T+A)/2}.

minor comments (5)

[Sec. VII-A] The main text does not include the baseline comparisons that support the abstract's claim of 'better coverage than an aerial-only baseline under energy shortage and comparable performance under time shortage.' These results appear only in the supplementary (Tabs. 1-2). I recommend citing these tables explicitly in Sec. VII-A or moving a summary of the framework-level comparison into the main text.
[Sec. VII-B] The real-world experiment reports only one run. Please state the number of runs explicitly and, if multiple runs were performed, report the spread of energy/modality trajectories. Also, the energy and modality curves in Fig. 10(b) would be more informative if annotated with the times of takeoff/landing events and with the measured versus predicted remaining energy.
[Eq. (3)] The time cost model uses max(length/v_M,max, dyaw/omega_M,max). This neglects acceleration limits and turning time during path traversal. This is a simplification, but since the same model is used in simulation and planning, the resulting bias is only relevant to real-world transfer; please mention this limitation explicitly.
[Supplementary Sec. 9.1] The hyperparameter formulas a1 = b1 - log(0.3), b1 = log(10) should be checked for typographical consistency: the main text (Sec. V-A) defines kappa_Er(x) = exp(-a1*x/E_all + b1) and kappa_Tr(x) = exp(-a2*x/T_all + b2). Please clarify the units of b1 and b2 (the offset in the exponent must be dimensionless).
[Sec. II] Reference [6] is cited as the basis for the bimodal motion planner, but the extension over [6] is described only qualitatively. Please highlight the specific new capabilities (e.g., online ground segmentation, dual grid maps, modality-aware primitives, ESDF-based edge safety) in a way that lets readers identify the novel components without reading the supplementary.

Circularity Check

1 steps flagged · score 4.0 of 10

Simulation energy/time metrics are computed from the same constant-power model that BM-MCTS optimizes, so the resource-balancing demonstration is partly self-confirming; the real-world run and coverage comparisons provide independent content.

self definitional [Eq. (4); Sec. V-A Eqs. (7)-(8); Sec. VII-A simulation setup; Fig. 8(b)]
"Based on our TABV platform, the power of the aerial modality is 7 times that of the terrestrial modality. So we set PT = 1, and PA = 7, meaning that one second of movement in terrestrial mode consumes 1 unit of energy, while one second in aerial mode consumes 7 units."

The planner in Sec. V-A computes Er/Tr via Eqs. (7)-(8) using the constant-power model E = P_M * T from Eq. (4), and the simulation in Sec. VII-A adopts the same model with PT=1 and PA=7. The reported remaining energy and time when exploration is completed (Fig. 8(b)) are therefore produced by the very cost function BM-MCTS optimizes: the demonstrated budget-adaptation (larger time budget leading to more remaining energy, and vice versa) is a direct consequence of the objective rather than an independent test of the energy model. The single real-world run and the baseline coverage comparisons preserve some independent content, but the resource-balancing claim is self-confirming within simulation.

full rationale

The central claim has independent content: coverage comparisons against FUEL and UAV/UGV baselines (supplementary Sec. 8) use the same simulation model but show the planner's modality choices improve coverage, and the real-world experiment (Sec. VII-B) demonstrates feasibility on hardware. However, the paper's demonstration that BM-MCTS adapts to energy and time budgets rests on a simulation whose energy/time bookkeeping is identical to the planner's cost model (Eq. 4), so the resource-balancing results are partly by construction. The paper itself acknowledges the model's idealization in supplementary Sec. 4 ('The estimated energy and time consumption may not fully reflect real-world factors such as terrain resistance or aerodynamic effects') and lists 'Locally myopic resource estimation' as a limitation (Sec. 11), which supports treating this as a partial circularity rather than a fatal one. No load-bearing self-citation chain or imported uniqueness theorem was found; the motion planner citations [6, 26] are to prior published work and are supplemented by new enhancements.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim rests on a measured but simplified constant-power energy model, several hand-set hyperparameters and thresholds, and standard submodular and MCTS assumptions. No new physical entities are introduced.

free parameters (6)

Simulation power values P_T and P_A = P_T=1, P_A=7 (measured real ratio approximately 7.2)
Energy model Eq. (4) uses fixed average power per modality; in simulation these constants define all energy costs and the reported remaining energy.
Penalty hyperparameters a1, b1, a2, b2 = a1=b1-log(0.3), b1=log(10); a2=b2-log(0.7), b2=log(3)
Chosen by hand to make the energy penalty steeper than the time penalty; they directly control the planner's resource trade-off.
IG discount factor gamma_IG = 0.8
Used in backpropagation (Alg. 2) to discount distant information gain; hand-set and affects tree rewards.
Normalization floor epsilon = 0.05
Minimum normalized gain in the UCB score (Eq. 6a); hand-set.
Viewpoint coverage stopping thresholds = 95% coverage; minimum marginal gain 15% of cluster size
Used in the greedy viewpoint selection (supplementary Alg. 1); ad hoc and determines the size of viewpoint groups.
Average-modality parameters for guidance paths = v=(v_T+v_A)/2, P=(P_T+P_A)/2
The simulation step estimates costs between unexpanded viewpoint groups using an average of terrestrial and aerial modalities instead of the true unknown modality, an approximation acknowledged in Sec. 5 of the supplementary.

assumptions (6)

standard math Greedy maximization of a submodular set function is within (1-1/e) of the optimal solution
Invoked in Sec. IV-A and supplementary Sec. 1 to justify greedy viewpoint selection for coverage.
standard math MCTS with UCB selection converges to the optimal decision under the given reward model
Assumed for BM-MCTS; based on the cited Pareto MCTS work [25], not proved in this paper.
domain assumption A constant average power per modality faithfully models actual energy consumption
Used in Eq. (4); both the planner's estimates and the simulation's reported energy use this model, and terrain or speed variations are ignored.
domain assumption Topo-graph A* search gives a conservative and sufficiently accurate path length estimate
Employed in the simulation phase (Sec. V-C and supplementary Sec. 3) to estimate costs between viewpoint groups; accuracy is not quantified.
domain assumption Ground traversability can be inferred from grid-map ground segmentation
Underlies terrestrial viewpoint generation and motion planning; supplementary Sec. 11 (limitations) states this is a simplification.
ad hoc to paper The average modality (T+A)/2 approximates the unknown modality choices in guidance paths
Sec. V-C; introduced to make the grouped TSP computable, with the paper admitting it introduces estimation inaccuracy.

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Cite this review

Pith. "Pith review of Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles." pith.science (2026). https://pith.science/paper/2YKQON5B

@misc{pith2026250721338,
  author       = {Pith},
  title        = {Pith review of: Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2YKQON5B}},
  note         = {Machine review of arXiv:2507.21338}
}

read the original abstract

Terrestrial-aerial bimodal vehicles, which integrate the high mobility of aerial robots with the long endurance of ground robots, offer significant potential for autonomous exploration. Given the inherent energy and time constraints in practical exploration tasks, we present a hierarchical framework for the bimodal vehicle to utilize its flexible locomotion modalities for exploration. Beginning with extracting environmental information to identify informative regions, we generate a set of potential bimodal viewpoints. To adaptively manage energy and time constraints, we introduce an extended Monte Carlo Tree Search approach that strategically optimizes both modality selection and viewpoint sequencing. Combined with an improved bimodal vehicle motion planner, we present a complete bimodal energy- and time-aware exploration system. Extensive simulations and deployment on a customized real-world platform demonstrate the effectiveness of our system.

Figures

Figures reproduced from arXiv: 2507.21338 by the authors.

**Figure 2.** (a): An overview of the proposed TABV exploration framework. (b): Module details: [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** An illustration of the process of BM-MCTS. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (23 more)

**Figure 4.** Figure 4: An example for determining the potential children. (a): The [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 6.** Figure 6: The hierarchical bimodal motion planning framework. (a): The kinodynamic path search front end. (b): The flatness-differential [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: (a) A two-story house scene for TABV exploration. (b) Four exploration phases. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: (a)-(b): Performance under different energy/time budgets. (a): The ratio of modality under different budgets, where the value [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Simulation scenes. (a): A multi-room scene with a 0.5m height partition wall. (b): A scene with a series of viewpoints. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

**Figure 10.** Figure 10: Real-world experiment. (a): Side view of the entrance to the underground parking garage. (b): The energy and modality curves. [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]

**Figure 1.** Figure 1: (a): The pipeline of bimodal viewpoint generation with two coverage strategies. [PITH_FULL_IMAGE:figures/full_fig_p010_1.png]

**Figure 2.** Figure 2: Ground extraction and ground ESDF generation. (a). Grid map. (b). Extracted [PITH_FULL_IMAGE:figures/full_fig_p012_2.png]

**Figure 3.** Figure 3: Grid map generation for different modalities. (a). The voxel grid map before [PITH_FULL_IMAGE:figures/full_fig_p012_3.png]

**Figure 4.** Figure 4: Results of the bimodal motion planner in simulation. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: Real-world multi-layer motion planning experiment. (a). The planned bimodal [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: (a). The exploration scene in the Gazebo simulator. (b). The result of topo-graph [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

**Figure 7.** Figure 7: Exploration with energy budget = 25, which much less than the amount for [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

**Figure 8.** Figure 8: Gazebo simulation. (a). A large two-story house scene. (b). The TABV model [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

**Figure 9.** Figure 9: Results of the two-story house exploration. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

**Figure 10.** Figure 10: Exploration process of Case 1 (E300, T400) in the simulation. (a)(c). Result [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

**Figure 11.** Figure 11: Computation time comparison between FUEL and ours. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png]

**Figure 12.** Figure 12: Modality configuration comparisons under two cases. (a). Case 1, where energy [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]

**Figure 13.** Figure 13: Resource-aware decision-making algorithm comparisons (a). The simulation [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]

**Figure 14.** Figure 14: Computation time comparison among NBV-based, TSP-based and our algo [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]

**Figure 15.** Figure 15: Penalty functions of reamining energy and time. [PITH_FULL_IMAGE:figures/full_fig_p026_15.png]

**Figure 16.** Figure 16: The hardware of the TABV. Although the sensor remains the same, the effective FoV differs between modalities due to its vertically symmetric mounting on the TABV. This design balances two competing needs: i) In terrestrial modality, more downward visibility is needed …

**Figure 17.** Figure 17: The effective FoV of different modalities. [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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4" FUNCTION default.is.dash.repeated.names #1 FUNCTION default.name.format.string

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