Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation

Ignacio Carlucho; Maria Koskinopoulou; Markus Buchholz; Michele Grimaldi; Yvan R. Petillot

REVIEW 5 major objections 5 minor 1 cited by

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation

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

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Pith's one-line read Caching and reusing 'highway' paths on a dense roadmap cuts the exponential cost of repeated underwater motion planning while keeping trajectory fidelity close to baseline PRM.

desk verdict A sensible caching-on-PRM idea that is undercut by an invalid path-splice in Algorithm 1 and a Bayesian network that never materializes. read the letter →

arxiv 2507.14099 v1 pith:W7XBJBIW submitted 2025-07-18 cs.RO

Markus Buchholz , Ignacio Carlucho , Michele Grimaldi , Maria Koskinopoulou , Yvan R. Petillot This is my paper · ORCID

classification cs.RO

keywords underwatermanipulationmotionplanningProbabilisticRoadmap(PRM)HeuristicSpaceBayesiannetworkexperiencereuseA*searchvehicle-manipulatorsystem

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 sets out to break the usual trade-off in Probabilistic Roadmap (PRM) planning: dense sampling is needed to thread cluttered underwater workspaces, but dense roadmaps make each A* query exponentially expensive. Its proposal, the Adaptive Heuristic Motion Planner (AHMP), keeps the dense roadmap and layers a Heuristic Motion Space (HMS) on top of it—a memory of 'highway' nodes and previously successful paths. For each new goal, the planner selects a cached highway approach node, runs two short A* searches from the current state to that node and from the node to the goal, and splices the cached path between them; only if a segment fails does it fall back to full A*. The claimed payoff is that per-goal cost drops from $O(b^d)$ to roughly $O(b^{d'})$ with $d'\ll d$, so multi-goal tasks like underwater welding or inspection stay real-time even at 30,000 samples, while tank experiments on a vehicle–manipulator system show joint trajectories within about 0.28–0.39 rad of baseline PRM. A Bayesian Network is meant to feed uncertainty estimates from live sensor data into the choice of cached path, giving the planner a way to favor routes that are not just short but likely to succeed.

What carries the argument

The carrying mechanism is the Heuristic Motion Space (HMS)—a cache of highway nodes and the paths connecting them, layered on top of the base PRM rather than replacing it. Each stored motion primitive $M_i$ carries an uncertainty estimate $U_i$; selection on a query is driven by the score $\exp(-\lambda U_i)/(1+d(M_i,G))$ with a distance threshold $\tau$ (Algorithm 1), and the planner executes A* only on the two residual segments $(current \to oNode)$ and $(oNode \to goal)$. The exponential speedup claim rests on the depth reduction: the cached highway replaces the deepest part of the search, so effective A* depth falls from $d$ to $d'\ll d$ and the time ratio $T_{\mathrm{A}^*}/T_{\mathrm{HMS}}\approx b^d/b^{d'}$ follows. A Bayesian Network—a directed acyclic graph over environment and robot variables, with the stated factorization $P(X_1,\dots,X_n)=\prod_i P(X_i \mid \mathrm{Pa}(X_i))$—is included to update the uncertainty terms from real-time sensor data, and after each successful plan the stored probabilities are reweighted by $p_{\mathrm{new}}(u)\propto p_{\mathrm{old}}(u)\exp(-\alpha\|\Pi_i\|)$ and renormalized. The system also caches every new path under the goal at weight 1.0, so the memory grows with mission experience.

What would settle it

Ablate the learned selection: replace the scoring rule with uniform-random choice of a cached node within the same distance threshold, keeping the same stitch-and-fallback logic. If random caching matches the reported speedups and the joint-error fidelity of Table I in the same tank scenario, then the ranking—and the Bayesian updates behind it—are not what carries the result. Separately, instrument the planner to measure effective A* expansion depth $d'$ with and without the HMS on identical goal sets; the claimed time ratio $b^d/b^{d'}$ makes a quantitative prediction that can be checked directly.

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Extended reading notes

Core claim

Stated on the paper's own terms, the discovery is that memory can substitute for search without sacrificing coverage. The AHMP deliberately builds a dense PRM—up to tens of thousands of collision-free samples—so that narrow passages and fine obstacle clearance are represented, then overlays a Heuristic Motion Space that records frequently traversed sub-paths (motion primitives) and the highway nodes they connect. On a query, the planner scores each cached primitive by $P(M_i \mid G, E) \propto \exp(-\lambda U_i)/(1 + d(M_i, G))$, where $U_i$ is the primitive's uncertainty estimate and $d(M_i, G)$ its distance to the goal in configuration space; the best-scoring node within a threshold $\tau$ becomes the approach node, and the final trajectory is the concatenation of two short A* segments with the cached highway path. The paper's complexity argument is that this replaces one deep search per goal with two shallow ones: total cost goes from $N \cdot O(b^d)$ to $N \cdot O(|HMS|) + \sum_i O(b^{d'_i})$ with $d'_i \ll d$, so the time ratio $T_{\mathrm{A}^*}/T_{\mathrm{HMS}} \approx b^d / b^{d'}$ grows exponentially in the depth saved. Experiments in a tank with a BlueROV2 and Reach Alpha 5 arm show the AHMP tracking baseline PRM joint trajectories with mean absolute errors around 0.28–0.39 rad across five test motions, while RRT deviates more; in simulation, AHMP runtime stays stable as sample count and goal count grow, ending near 2.2 s where plain PRM+A* reaches about 5.6 s at 30,000 samples.

Load-bearing premise

The speedup rests on the unverified assumption that the hand-crafted selection score $\exp(-\lambda U_i)/(1+d(M_i,G))$ with threshold $\tau$ reliably picks cached highway paths whose stitched segments match full A* quality—and that the Bayesian Network, whose structure and update rules are never specified, supplies meaningful uncertainty values $U_i$ from sensor data.

Editorial extensions

If this is right

Multi-goal missions in cluttered environments—underwater spot welding, inspection, cleaning—can keep real-time planning at roadmap densities where naive PRM+A* becomes impractical: at 30,000 samples the reported runtime is about 2.2 s versus 5.6 s for plain A*.
The speedup does not come from thinning the roadmap, so dense-sample safety clearance and narrow-passage connectivity are preserved; trajectory accuracy stays close to baseline PRM (mean joint error roughly 0.28–0.39 rad in the tank tests).
When no cached highway is usable, the method degrades gracefully to full A*, so a cold start or a changed obstacle layout does not leave the planner without a path.
Because the HMS accumulates experience online with no training phase, the planner can carry its memory into unexplored environments: each executed plan enriches the cache that speeds up later queries.

Reading between the lines

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

A direct consequence the paper does not state: the speedup should be largest for goals near previously cached highways and negligible for the first query in an empty cache, so missions could deliberately warm up the HMS with bootstrapping goals to maximize the gain.
A natural experiment the paper leaves open: freeze the Bayesian updates and compare cached-path selection against the live-BN version on the same sensor stream, to isolate how much the Bayesian Network contributes beyond the geometric score.
The same highway-caching pattern could transfer beyond underwater systems to any manipulator with recurring goal sets—warehouse arms, inspection robots—where the configuration space is cluttered and the same sub-paths are traversed repeatedly.
The validation is kinematic, with the vehicle stabilized so arm motion is decoupled from the base; extending the planner to whole-body vehicle–manipulator coordination during grasping would test whether cached paths remain safe when the base moves.

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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

Two things to know about this paper. First, it has a real, demonstrable bug in the central algorithm: the path-reuse step in Algorithm 1 concatenates a cached path that starts at an old origin, not at the current node, so the output is not a continuous route. That is load-bearing, because the entire efficiency claim rests on this splice. Second, the Bayesian network described in the abstract and architecture is never actually defined—no structure, no CPTs, no update equations—so the 'adaptive' half of the method is not reproducible.

What is new: the specific combination of a PRM with a cached-highway memory and a hand-crafted selection rule for an underwater vehicle-manipulator system. The simulation shows a clear scaling advantage at high sample counts (2.2s vs 5.6s at 30k samples), and the tank experiments demonstrate that the planned trajectories closely match baseline PRM. The authors are honest about the fact that speed gains in the tank are implied, not measured.

The soft spots beyond the algorithm bug: the complexity ratio in Section III-D is largely a restatement of the caching idea—of course reusing paths avoids searching, so the ratio is not an independent discovery. The composite cost function promised in the abstract never appears. Fig. 4 lacks error bars despite averaging five runs. Most importantly, the two closest experience-based baselines (Motion Memory, EB-RRT) are cited but never compared, so the incremental value is unclear.

That said, the core caching idea is defensible, the paper is clearly written, and all the missing pieces are addressable in a revision. This is not a desk-reject; it deserves serious refereeing. A referee should ask for: a corrected algorithm with a clear semantics for cached path reuse, either a real BN or removal of that claim, error bars, and those baseline comparisons. I would not accept it as is.

Who it is for: roboticists working on practical manipulation planning, especially underwater, who want a lightweight experience-based planner. I'd bring it to a reading group to dissect the algorithm, but I probably wouldn't cite it in my own work until the details are fixed.

Referee Report

5 major / 5 minor

Summary. The paper proposes the Adaptive Heuristic Motion Planner (AHMP), which layers a Heuristic Motion Space (HMS) on top of a dense PRM roadmap to cache and reuse previously computed 'highway' paths for repeated or multi-goal motion planning in underwater manipulation tasks. A Bayesian Network is said to update uncertainty estimates from sensor data and to bias the selection of cached paths. The central claims are that AHMP reduces the effective A* search depth from d to d' << d, yielding the approximate speedup ratio T_A*/T_HMS ≈ b^d / b^{d'} in Section III-D, and that it produces trajectories of accuracy comparable to plain PRM while being more scalable. The paper reports simulation comparisons against PRM+A* and RRT and physical tank experiments with a BlueROV2 and Reach Alpha 5 manipulator.

Significance. The idea of caching and reusing high-value motion segments for repeated manipulation tasks is practically relevant for underwater inspection and welding scenarios, and the paper includes a real-hardware demonstration, which is a strength. However, the central efficiency claim currently rests on an invalid path-splicing operation in Algorithm 1 and on an unverified depth-reduction argument in Section III-D. The Bayesian Network component, which is central to the claimed adaptability, is specified only at the level of a generic factorization, with no structure, parameters, or update rules given. If these load-bearing gaps were repaired, the approach could be a useful contribution, but as written the manuscript does not establish the claimed speedup or the role of the probabilistic components.

major comments (5)

[Algorithm 1, lines 7-11] The cached-path splice is not a valid path operation as written. Line 21 caches the full planned path under the goal key: HMS[gi] ← (Πi, 1.0). On a later query, line 7 retrieves HMS[oNode].path, which is therefore a path from whatever node was current at caching time to oNode, not a path from oNode to the start of PartialB. The concatenation PartialA ∥ HMS[oNode].path ∥ PartialB in line 11 is therefore discontinuous and may contain backtracking. The paper never defines an alternative semantics for HMS[oNode].path or provides a splice/continuity condition. Since the entire speedup claim depends on reusing this path, this is a load-bearing flaw that must be fixed by specifying the cached path representation (e.g., storing paths by origin and destination) and by proving that the concatenation is a valid continuous path in the current environment.
[Section III-D, Eq. (ratio)] The claimed time ratio T_A*/T_HMS ≈ b^d / b^{d'} is not established by the algorithm. The reuse of a cached path is the mechanism, but line 8 still requires A*(E, currentNode, oNode), and line 9 requires A*(E, oNode, gi). If oNode is selected only by proximity to the goal, the segment from currentNode to oNode may require a full-depth search of the roadmap, so d' is not bounded by construction. Moreover, the ratio as stated is largely a restatement of the caching design rather than an independently verified empirical result. The paper should either provide a formal bound on d' under the selection criterion or report measured search-node expansions and wall-clock times across the full goal sequence, including the cost of populating the HMS.
[Section III-C, selection probability and BN] The selection model is underspecified. The formula P(Mi | G, E) ∝ exp(−λ Ui) / (1 + d(Mi, G)) uses Ui, λ, and d(Mi, G), but Ui is never defined operationally, λ is never set or analyzed, and no sensitivity or ablation is given. The Bayesian Network is described only through the generic factorization P(X1,...,Xn) = ∏ P(Xi | Pa(Xi)); the DAG structure, the random variables, the conditional probability tables, and the update rule from sensor data are never specified. Because the paper claims that BN updates refine path success probabilities and guide real-time adaptation, this is load-bearing. The authors should fully define the BN and provide an ablation comparing the proposed selection with distance-only and random selection to show that the probabilistic ranking is what provides the benefit.
[Abstract and Introduction, composite cost function] The paper repeatedly states that paths are optimized by minimizing a composite cost function accounting for distance, uncertainty, energy consumption, and execution time (Abstract and Section I), but no such cost function is defined anywhere in the Methodology. Algorithm 1 calls A* without specifying an edge-cost or heuristic function, and Section III-C only describes a probability proxy for selecting cached nodes, not a path cost. This mismatch makes it impossible to reproduce the planner or interpret the reported runtimes. Either define the composite cost and its use in A*, or remove this claim and state the actual objective.
[Section IV and Table I] The experimental evidence for the real-time efficiency claim is incomplete. Figure 4 shows mean execution times over 5 runs but no error bars or per-run variation, so the claimed stability of AHMP is not statistically supported. In the tank experiments, the speed gains are explicitly deferred: the text says 'not explicitly shown here but implied by the use of HMS' (Section IV-B). Table I reports 'Mean Abs. Error Across Joints' but does not clearly define the error: whether it is the difference between planned and executed joint trajectories, or between the PRM and AHMP paths. These omissions matter because the accuracy result is one of the two central claims. Please report distributions, define the error metric, and provide actual runtime measurements from the hardware experiments.

minor comments (5)

[Section III-D] Typo: 'With reward to runtimes' should be 'With regard to runtimes'.
[Section IV-A] The parameter 'max iter rrt' is mentioned but never defined, and the figure legend does not state the RRT iteration counts corresponding to the four colors.
[Section II] The related-work discussion references several memory-based planners, including Motion Memory and EB-RRT, but the experimental comparison includes only PRM+A* and RRT; a direct comparison with at least one of these memory-based planners would strengthen the claims about the contribution of HMS.
[Section IV-B] In Table I, the standard deviation columns show large run-to-run variation relative to the mean errors; please clarify whether the table reports errors across joints, across goals, or across trials, and correct the caption accordingly.
[Section III-C] The notation is inconsistent: the algorithm uses 'BN' as an input to the heuristic selection, while the text describes a probabilistic evaluation P(u | gi, BN); the relation between the BN output and the heuristic function heuristic(u, gi) is never defined.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the AHMP speedup is supported by empirical benchmark comparisons and an analytic complexity bound, not by a fitted parameter relabeled as a prediction or by a load-bearing self-citation chain.

full rationale

The paper's central efficiency claim rests on two independent pillars. First, Fig. 4 and the surrounding discussion provide empirical runtime comparisons of AHMP against PRM+A* and RRT across varying max samples and goal counts; these comparisons are external to the method's own assumptions and are not derived from the same data used to fit any parameter. Second, Section III-D gives an analytic complexity estimate, TA*/THMS ≈ b^d / b^{d'}, which is a direct consequence of the algorithm reusing cached path segments rather than an independent prediction; an algorithmic complexity bound of this kind is not circular merely because it follows from the algorithm's design. The selection probability P(Mi | G, E) ∝ exp(−λ Ui) / (1 + d(Mi, G)) and the threshold τ are explicitly stated modeling assumptions, not outputs derived from the target result; they may be arbitrary or under-justified, but that is a weakness of the model, not a circular reduction. The Bayesian network is under-specified (no structure, conditional probability tables, or update rules are given), and the tank section explicitly admits that speed gains are 'not explicitly shown here but implied by the use of HMS'; these are evidentiary and completeness gaps, not cases where an input is renamed as a prediction. The only self-citations, [8] and [24], are background references that do not carry the argument. A separate correctness concern exists in Algorithm 1, where splicing HMS[oNode].path between two A* segments may not yield a continuous path because the cache key is the destination, but this is an algorithmic defect rather than a circular step. No equation in the paper reduces by construction to another claimed output, and no fitted parameter is relabeled as a predicted quantity. Therefore the honest finding is that the derivation chain is not circular, and no specific circular step can be exhibited. The appropriate score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. It does introduce an algorithmic construct (Heuristic Motion Space) and a hand-crafted probabilistic selection, with three unspecified parameters and an unspecified Bayesian network, which together carry the load of the claimed adaptability. The free parameters are not fitted to data in a documented way, and the axioms above are the unstated assumptions on which the performance claims rest.

free parameters (3)

lambda (λ)
Confidence parameter in the selection probability P(Mi|G,E) ∝ exp(-λ Ui)/(1+d(Mi,G)). No value or fitting procedure is given; it directly controls which cached paths are selected.
tau (τ)
Distance threshold that defines which HMS nodes are considered as approach points in Algorithm 1, line 5. Not specified; it controls the trade-off between using cached paths and falling back to full A*.
alpha (α)
Decay factor in the update pnew(u) ∝ pold(u) exp(-α||Πi||) that re-ranks cached paths after each use. Not specified; it determines how quickly old paths are deprioritized.

assumptions (4)

ad hoc to paper The selection formula P(Mi|G,E) ∝ exp(-λ Ui)/(1+d(Mi,G)) is an accurate proxy for the probability that a cached path leads to the goal in the current environment.
Stated in Section III-C without derivation, validation, or sensitivity analysis. The entire adaptive behavior of AHMP depends on this assumed ranking.
ad hoc to paper Stitching a cached path with A* segments from the start to oNode and from oNode to the goal yields a path of quality comparable to a full A* path.
Algorithm 1 lines 7-11 assume that the concatenated path is acceptable. The paper provides no suboptimality bound or empirical check of stitching quality.
domain assumption A* complexity is O(b^d) and partial searches with reduced depth d' << d dominate the runtime of AHMP.
Section III-D uses this to claim a speedup ratio. The depths d and d' are never measured, and the exponential complexity model for A* on a PRM is a simplified assumption.
ad hoc to paper The Bayesian network can be updated with sensor data and meaningfully refines path success probabilities.
The BN is mentioned in Section III-B and Algorithm 1 line 4, but its nodes, edges, conditional probability tables, and update equations are never specified. The presented selection formula does not use any BN outputs.

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Cite this review

Pith. "Pith review of Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation." pith.science (2026). https://pith.science/paper/W7XBJBIW

@misc{pith2026250714099,
  author       = {Pith},
  title        = {Pith review of: Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W7XBJBIW}},
  note         = {Machine review of arXiv:2507.14099}
}

read the original abstract

Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous underwater manipulation. Our approach employs the Probabilistic Roadmap (PRM) algorithm within HMS to optimize paths by minimizing a composite cost function that accounts for distance, uncertainty, energy consumption, and execution time. By leveraging HMS, our framework significantly reduces the search space, thereby boosting computational performance and enabling real-time planning capabilities. Bayesian Networks are utilized to dynamically update uncertainty estimates based on real-time sensor data and environmental conditions, thereby refining the joint probability of path success. Through extensive simulations and real-world test scenarios, we showcase the advantages of our method in terms of enhanced performance and robustness. This probabilistic approach significantly advances the capability of autonomous underwater robots, ensuring optimized motion planning in the face of dynamic marine challenges.

Figures

Figures reproduced from arXiv: 2507.14099 by the authors.

**Figure 2.** Conceptual overview of the AHMP. The algorithm starts by building a PRM (Left). As more paths are explored, a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The general architecture of a UVMS outlining the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Comparison of execution times for pure PRM + A* [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Snapshots from the experiments illustrate the progression of the UVMS motion. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Joint position trajectories (10 consecutive goals per [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Distributed AI Agents for Cognitive Underwater Robot Autonomy
cs.RO 2025-07 reject novelty 6.0 of 10

UROSA controls underwater robots with distributed LLM/VLM agents, retrieval memory, and runtime code generation; feasibility is shown, but the claimed advantage over classical planners is not.

Reference graph

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A multi-heuristic search- based motion planning for automated parking,

B. Adabala and Z. Ajanovi ´c, “A multi-heuristic search- based motion planning for automated parking,” in 2023 XXIX International Conference on Information, Commu- nication and Automation Technologies (ICAT) . IEEE, Jun. 2023, p. 1–8

2023
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Search-based motion planning with task-space motion primitives for hyper-redundant manipulators in cluttered environment,

J. Wang, R. Miao, J. Ma, and H. Shi, “Search-based motion planning with task-space motion primitives for hyper-redundant manipulators in cluttered environment,” in 2024 6th International Conference on Reconfigurable Mechanisms and Robots (ReMAR) , 2024, pp. 554–559

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A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning,

R. Bernardo, J. M. Sousa, and P. J. Gon c ¸alves, “A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning,” Computers & Industrial Engineering , vol. 182, p. 109345, 2023

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Blue robotics – high-quality marine robotics components,

Blue Robotics, “Blue robotics – high-quality marine robotics components,” https://bluerobotics.com/, 2024

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Reach robotics – underwater manipula- tors,

Reach Robotics, “Reach robotics – underwater manipula- tors,” https://reachrobotics.com/, 2024

2024
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Ceres Solver,

S. Agarwal, K. Mierle, and T. C. S. Team, “Ceres Solver,” 10 2023. [Online]. Available: https://github.com/ ceres-solver/ceres-solver

2023

Pith tools

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

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Arc bubble edge detection method based on deep transfer learning in underwater wet welding,

B. Guo and X. Li, “Arc bubble edge detection method based on deep transfer learning in underwater wet welding,” Scientific Reports , vol. 14, no. 1, p. 22628, 2024, published: 2024/09/30

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Robotic welding techniques in marine structures and production processes: A systematic literature review,

S. I. Wahidi, S. Oterkus, and E. Oterkus, “Robotic welding techniques in marine structures and production processes: A systematic literature review,” Marine Structures, vol. 95, p. 103608, 2024

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A survey of optimization-based task and motion planning: From clas- sical to learning approaches,

Z. Zhao, S. Cheng, Y . Ding, and Z. Zhou, “A survey of optimization-based task and motion planning: From clas- sical to learning approaches,” IEEE/ASME Transactions, 2024

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Mobile robotics and 3d printing: Addressing challenges in path planning and scalability,

M. Rastegarpanah, M. E. Asif, J. Butt, and H. V oos, “Mobile robotics and 3d printing: Addressing challenges in path planning and scalability,” Virtual and Physical Prototyping, 2024

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Review on motion planning of robotic manipulator in dynamic environments,

J. Liu, H. J. Yap, and A. S. M. Khairuddin, “Review on motion planning of robotic manipulator in dynamic environments,” Journal of Sensors , 2024

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Memory-based stochastic tra- jectory optimization for manipulator obstacle avoiding motion planning,

Y . Wang and X. Guo, “Memory-based stochastic tra- jectory optimization for manipulator obstacle avoiding motion planning,” in 2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) , 2022, pp. 188–194

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Inspection and maintenance of industrial infrastructure with autonomous underwater robots,

F. Nauert and P. Kampmann, “Inspection and maintenance of industrial infrastructure with autonomous underwater robots,” Frontiers in Robotics and AI, vol. 10, p. 1240276, 2023

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Autonomous underwater manipulation: Current trends in dynamics, control, planning, perception, and future directions,

E. Morgan, I. Carlucho, W. Ard, and C. Barbalata, “Autonomous underwater manipulation: Current trends in dynamics, control, planning, perception, and future directions,” Current Robotics Reports , vol. 3, no. 4, pp. 187–198, 2022

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