Robotics

21 pages, 10156 KB

Open AccessArticle

ROS2-Based Low-Cost Mobile Robot for Educational Assistance with Reactive Navigation and Semantic-Cached Language Processing

by Sebastián Alexis Aucapiña, Nataly Cecilia Benalcázar, José Varela-Aldás and Ramiro Isa-Jara

Robotics 2026, 15(7), 131; https://doi.org/10.3390/robotics15070131 - 8 Jul 2026

Abstract

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide [...] Read more.

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. Full article

(This article belongs to the Section Educational Robotics)

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17 pages, 8960 KB

Open AccessArticle

Experimental Validation of ASSIST-FEEv3 Elbow Assisting Device with Physiotherapy Considerations

by Cuauhtémoc Morales-Cruz, Fortunato Frisina, Francesco Scerbo, Rocco Mazzotta and Marco Ceccarelli

Robotics 2026, 15(7), 130; https://doi.org/10.3390/robotics15070130 - 3 Jul 2026

Abstract

Upper-limb rehabilitation and elderly exercise programs require lightweight, reliable, and physiotherapy-oriented assistive technologies capable of supporting controlled joint motion while enabling objective performance assessment. This paper presents experimental validation of ASSIST-FEEv3, a cable-driven elbow assisting device that is designed for flexion–extension exercises with [...] Read more.

Upper-limb rehabilitation and elderly exercise programs require lightweight, reliable, and physiotherapy-oriented assistive technologies capable of supporting controlled joint motion while enabling objective performance assessment. This paper presents experimental validation of ASSIST-FEEv3, a cable-driven elbow assisting device that is designed for flexion–extension exercises with emphasis on usability, portability, and physiotherapy integration. The device employs a dual-cable antagonistic mechanism that is actuated by servomotors housed in a compact module, allowing guided motion in the arm sagittal plane with minimal wearable load mass. A testing campaign was conducted with 25 healthy volunteers under the supervision of physiotherapy experts following a properly designed protocol for three sessions of ten repetitions each. Joint kinematics was acquired through integrated sensing, and performance metrics including maximum flexion, maximum extension, and range of motion (ROM) were analyzed to assess repeatability, motion smoothness, and user-specific variability. The results demonstrate consistent motion assistance across repeated cycles, variability between sessions, and comparable ROM distributions between sexes. Observed deviations were considered due to individual temporary conditions rather than device-related limitations. The device operated with low energy consumption as required in home-based applications. Test findings validate both the mechanical reliability and the physiotherapy-oriented operational framework of the ASSIST-FEEv3 device. Full article

(This article belongs to the Special Issue Selected Papers from MESROB2025: 9th IFToMM International Workshop on New Trends in Medical and Service Robotics)

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27 pages, 8168 KB

Open AccessArticle

Flexipede: A Bio-Inspired, Modular Myriapod Robot for Rough-Terrain Traversal

by Samudra Jit Saha, Md. Abid Chowdhury, Sayma Islam, Shamim Ahmed Deowan, Shifat E. Arman and Abhishek K. Ghosh

Robotics 2026, 15(7), 129; https://doi.org/10.3390/robotics15070129 - 1 Jul 2026

Abstract

Rough-terrain exploration is critical for applications ranging from post-disaster search-and-rescue to planetary exploration. While conventional wheeled or bipedal robots often struggle in these environments, biological organisms like myriapods demonstrate superior adaptability. Inspired by this, we present Flexipede—a compact, modular robotic system that employs [...] Read more.

Rough-terrain exploration is critical for applications ranging from post-disaster search-and-rescue to planetary exploration. While conventional wheeled or bipedal robots often struggle in these environments, biological organisms like myriapods demonstrate superior adaptability. Inspired by this, we present Flexipede—a compact, modular robotic system that employs a hybrid actuation architecture, wherein each module integrates a single actuator for propulsive gait generation and a secondary actuator to enable distributed yaw control. The platform is fully 3D-printable and cost-effective, with a fabrication cost of approximately $58 for the primary unit and $10 per additional module. Analytical kinematic modeling was employed to optimize linkage trajectories, with experimental results validating the system across six modular configurations and three distinct environments, including flat, rough, and inclined terrains. The platform achieved locomotion speeds up to 9 cm/s and navigated obstacles up to 32 mm high, while linkage path deviations remained functionally negligible (mean deviation of

3.24 %

) compared to analytical prediction. Turning performance converged with theoretical predictions as modular scaling increased, reaching a minimum deviation of

4.51 %

for the five-module configuration. Across all terrains, the system maintained a competitive average Cost of Transportation (CoT) of

18.01

, with stair climbing requiring a relatively higher CoT due to the elevated torque demands associated with vertical displacement. These results establish Flexipede as a high-performance benchmark for modular myriapod systems with significant potential for adaptive morphological research. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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24 pages, 8403 KB

Open AccessArticle

Depth-Assisted Sparse Visual Odometry for UAV-Relevant Synthetic RGB-D Evaluation: A Controlled Geometric-Backend Ablation

by Andrii Polukhin, Sergii Stirenko, Mairo Leier, Gert Jervan, Oleksandr Rokovyi, Oleg Alienin, Nazrul Nazeer and Yuri Gordienko

Robotics 2026, 15(7), 128; https://doi.org/10.3390/robotics15070128 - 30 Jun 2026

Abstract

Sparse visual odometry (VO) is a core component of lightweight unmanned aerial vehicle (UAV) visual navigation, yet the isolated effect of adding aligned metric depth to a minimal frame-to-frame pipeline is easily obscured in full SLAM systems. This paper presents a UAV-relevant controlled [...] Read more.

Sparse visual odometry (VO) is a core component of lightweight unmanned aerial vehicle (UAV) visual navigation, yet the isolated effect of adding aligned metric depth to a minimal frame-to-frame pipeline is easily obscured in full SLAM systems. This paper presents a UAV-relevant controlled synthetic ablation of RGB-only and RGB-D geometric backends under fixed sparse frontends. ORB matching and KLT tracking are evaluated on a 32-sequence TartanAir validation split of flight-like synthetic RGB-D scenes by routing identical 2D correspondences either to Essential Matrix estimation or, with aligned depth, to PnP with RANSAC. The study reports ATE, Sim(3)-aligned ATE, translational and rotational RPE, robustness under temporal subsampling and RGB degradations, and isolated solver latency on a workstation and Raspberry Pi 4. At stride 1, RGB-D PnP reduces ATE by 61.8% for KLT and 29.1% for ORB, with translational RPE reductions of 61.6% and 41.9%. Rotational RPE reductions are stronger and persist across all tested strides, reaching 85.9% for KLT and 77.3% for ORB at stride 1. Sim(3) analysis shows that only 7–16% of PnP ATE is metric-scale drift. At coarser strides, however, KLT-PnP no longer improves ATE, showing that depth assistance depends on stable frontend tracking and valid depth-supported correspondences. The contribution is a reproducible diagnostic benchmark and failure-mode analysis for UAV-relevant depth-assisted sparse VO under oracle aligned depth, providing component-level evidence rather than full onboard deployment validation. Full article

(This article belongs to the Special Issue UAV Systems and Swarm Robotics: 2nd Edition)

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24 pages, 5385 KB

Open AccessArticle

A Late-Fusion Multimodal Approach for Safety-Aware Workspace Modeling in Collaborative Robotic Systems

by Kevin David Ortega-Quiñones, Elias Escobar-Pereira, Michael Felipe Cifuentes-Molano, Germán Andrés Holguín-Londoño and Mauricio Holguín-Londoño

Robotics 2026, 15(7), 127; https://doi.org/10.3390/robotics15070127 - 30 Jun 2026

Abstract

Ensuring safe coexistence between human operators and industrial robot manipulators is a critical challenge in collaborative manufacturing environments. Existing approaches rely either on dedicated safety-rated hardware, which is expensive and difficult to retrofit, or on purely vision-based classifiers that discard the precise kinematic [...] Read more.

Ensuring safe coexistence between human operators and industrial robot manipulators is a critical challenge in collaborative manufacturing environments. Existing approaches rely either on dedicated safety-rated hardware, which is expensive and difficult to retrofit, or on purely vision-based classifiers that discard the precise kinematic state available from the robot controller, leading to unresolved visual ambiguities when different joint configurations produce similar appearances from fixed camera viewpoints. Kinematics-only approaches, while precise, lack the spatial context needed to disambiguate configurations near workspace boundaries. We propose RGBJointsNet, a late-fusion multimodal deep learning classifier that combines RGB visual features extracted by a frozen EfficientNet-B2 convolutional backbone with a compact kinematic stream processing the 12-dimensional joint angle vector of a dual-UR5 robotic cell. The model maps each observation to one of five mutually exclusive workspace zones: rest (

C_{0}

), nominal (

C_{1}

), extended (

C_{2}

), shared/collision-risk (

C_{3}

), and joint-limit/singularity (

C_{4}

). A dedicated simulation environment built on ROS 2 Humble Hawksbill and Gazebo Classic 11 was used to generate a labelled dataset of 54,309 frames and 162,927 RGB images from three calibrated overhead cameras, with analytic ground-truth labels derived from closed-form forward kinematics. Training on a CPU with a feature-caching strategy brings the per-epoch wall-clock time to seconds, making the approach tractable without GPU hardware. On the held-out test set, the model achieves 87.1% overall accuracy and a macro-averaged F1 score of 90.0%, with near-perfect recall of 99.3% for the safety-critical shared zone

C_{3}

. The trained classifier is integrated as an ROS 2 inference node capable of running at 10

Hz

on a standard workstation. Our results demonstrate that joint angle information is a decisive complement to RGB imagery for fine-grained, safety-oriented workspace classification in simulation-derived settings. Full article

(This article belongs to the Topic Advances in Robot Vision Perception and Control Technology)

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Graphical abstract

17 pages, 2431 KB

Open AccessArticle

Bilevel Trajectory Optimization for Vacuum-Based Pick-and-Place Operations: A Numerical Study

by Georg Steinert, Clemens Troll and Jens-Peter Majschak

Robotics 2026, 15(7), 126; https://doi.org/10.3390/robotics15070126 - 30 Jun 2026

Abstract

Motion optimization has a significant influence on performance and robustness of modern robotic handling systems. In this study, a pick-and-place operation, as can be found in many processing machines, serves as a representative use case to develop a novel method for motion optimization. [...] Read more.

Motion optimization has a significant influence on performance and robustness of modern robotic handling systems. In this study, a pick-and-place operation, as can be found in many processing machines, serves as a representative use case to develop a novel method for motion optimization. Based on optimal control theory, the introduced method uses bilevel optimization simultaneously addressing process stability, favorable dynamic behavior and practical applicability. A process model for gripper load estimation established in the literature serves both as a basis for optimization and for evaluating the solution found. To establish a benchmark, a spline-based trajectory is generated. As this work proposes a theoretical approach, simulations based on the retrieved model serve as an evaluation basis of the resulting trajectories. The results show that a significant reduction in gripper load by approximately 60% was achieved compared to the reference motions. Eventually, requirements and limitations for application of the new method are discussed. Full article

(This article belongs to the Section Industrial Robots and Automation)

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30 pages, 9839 KB

Open AccessArticle

A Learning Framework for Robust Navigation of Mobile Robots Under Partial Observability

by Truong Nhut Huynh, Caiden Sivak, Hector Gutierrez and Kim-Doang Nguyen

Robotics 2026, 15(7), 125; https://doi.org/10.3390/robotics15070125 - 30 Jun 2026

Abstract

Autonomous navigation in mobile robotics faces tremendous challenges from partial observability due to sensor degradations such as noise and flickering in laser scans. Traditional methods like Adaptive Monte Carlo Localization (AMCL) and Gmapping perform well in ideal conditions but fail under these sensor [...] Read more.

Autonomous navigation in mobile robotics faces tremendous challenges from partial observability due to sensor degradations such as noise and flickering in laser scans. Traditional methods like Adaptive Monte Carlo Localization (AMCL) and Gmapping perform well in ideal conditions but fail under these sensor degradations. This paper develops a unified framework that integrates reinforcement learning with temporal sequence modeling, augmented by high-level semantic reasoning and parameterized quantum representations within a coherent architecture, to enable robust navigation for mobile robots. The framework models navigation as a partially observable Markov decision process (POMDP) and analyzes degraded LiDAR scans and odometry to generate velocity commands for motion planning and mapping. Experiments in a sim-to-real platform across four environments and real-world tests in indoor offices, outdoor terrains, and dynamic parking lots demonstrate substantial improvements compared to state-of-the-art methods. Success rates increase by up to 45 percentage points in dynamic scenarios, path lengths shorten by 20–25%, and map accuracies improve by 40% compared to baselines. The proposed approach achieves these gains through quantum-enhanced feature extraction for exploration, temporal modeling for state correction, and semantic reasoning for obstacle interpretation. This work advances reliable robot autonomy in uncertain environments. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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18 pages, 13772 KB

Open AccessArticle

IPAnema Safe: A Cable-Driven Parallel Robot for Safe Operation Above People

by Christoph Martin, Marc Fabritius, Christian Lehnertz, Jakob Traub, Johannes T. Stoll, Werner Kraus and Andreas Pott

Robotics 2026, 15(7), 124; https://doi.org/10.3390/robotics15070124 - 30 Jun 2026

Abstract

Training ship crews for new scenarios at sea in reproducible sea conditions is weather-dependent and dangerous. The Maritime Research Institute Netherlands (MARIN) addresses this issue by offering a variety of different simulators to safely train people onshore. For scenarios involving a swinging hook [...] Read more.

Training ship crews for new scenarios at sea in reproducible sea conditions is weather-dependent and dangerous. The Maritime Research Institute Netherlands (MARIN) addresses this issue by offering a variety of different simulators to safely train people onshore. For scenarios involving a swinging hook or a ladder for a pilot boarding a ship in turbulent sea conditions, a safe cable-driven parallel robot, the IPAnema Safe, is developed. Its safety features allow its platform to move above people during training scenarios. Therefore, in addition to standard safety features and a proper mechanical design, the safety functions Safely Limited Position (SLP) and Safely Limited Speed (SLS) for a cable-driven parallel robot are implemented on safety-certified hardware with Performance Level e (PL e). This work presents the design process and implementation of the IPAnema Safe, focusing on its layout, winches, platform optimization and functional safety features. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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21 pages, 9002 KB

Open AccessSystematic Review

ROS-Enabled DIY and Open-Source Wheeled Robots for Higher Education Learning and Competitions: A Systematic Review

by Rúben Pereira, Benedita Malheiro and Manuel F. Silva

Robotics 2026, 15(7), 123; https://doi.org/10.3390/robotics15070123 - 30 Jun 2026

Abstract

This study systematically characterizes Do It Yourself (DIY) and open-source wheeled robotic platforms used in higher education and academic competitions. It also analyzes Robot Operating System (ROS)-based designs with respect to real-time performance and multi-sensor integration, following Preferred Reporting Items for Systematic Reviews [...] Read more.

This study systematically characterizes Do It Yourself (DIY) and open-source wheeled robotic platforms used in higher education and academic competitions. It also analyzes Robot Operating System (ROS)-based designs with respect to real-time performance and multi-sensor integration, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A total of 20 high-quality studies were identified across five major digital libraries (Dimensions, Web of Science, SpringerLink, ScienceDirect, and IEEE Xplore), which were searched on 12 January 2026. Eligibility was restricted to peer-reviewed English-language studies published between 2005 and 2026 that explicitly implement ROS-based wheeled platforms in higher education contexts. Results were synthesized through qualitative analysis using a structured data extraction form implemented in the Parsifal systematic review platform. Methodological quality and risk of bias were assessed using a structured appraisal checklist. The results show a dominant trend toward distributed dual-processor architectures, which separate low-level real-time control from high-level processing. Most platforms target an accessible price range of 50€ to 500€ for open-source and DIY platforms. ROS has emerged as the standard middleware, enabling multi-sensor integration and supporting digital twin workflows. There is also a clear shift toward open-source hardware and Three-Dimensional (3D)-printed modular designs, which reduce production costs. However, challenges remain, including software obsolescence and the lack of maintenance plans. The findings highlight the need for interoperable reference architectures and automated deployment workflows to ensure long-term sustainability. Evidence is limited by heterogeneity, inconsistent reporting, and small sample sizes, which introduce risks of bias and imprecision. This review was formally registered with protocols.io. Full article

(This article belongs to the Section Educational Robotics)

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33 pages, 55733 KB

Open AccessArticle

LE-HG-PRM: A Structure-Aware Roadmap Planner for Intelligent Warehouse Logistics

by Siyuan Wang, Gongsen Wang, Feng Yang, Dawu Peng, Xingyu Yan, Shuyi Zhang, Xinyi Li and Zhen Tian

Robotics 2026, 15(7), 122; https://doi.org/10.3390/robotics15070122 - 29 Jun 2026

Abstract

Efficient AGV/AMR path planning is essential for intelligent warehouse logistics, where regular shelves, narrow aisles, local bottlenecks, and heterogeneous obstacles strongly affect roadmap quality. This study proposes LE-HG-PRM, a structure-aware extension of heuristic-guided probabilistic roadmap planning. The method embeds warehouse geometric priors into [...] Read more.

Efficient AGV/AMR path planning is essential for intelligent warehouse logistics, where regular shelves, narrow aisles, local bottlenecks, and heterogeneous obstacles strongly affect roadmap quality. This study proposes LE-HG-PRM, a structure-aware extension of heuristic-guided probabilistic roadmap planning. The method embeds warehouse geometric priors into probability-field sampling, region-adaptive neighborhood connection, and cache-accelerated progressive path refinement. Compared with the preliminary conference version, the journal version introduces a redesigned warehouse-oriented planning framework and substantially expands the experimental validation. Four experimental campaigns are conducted, covering static-complexity progression, corridor-width sensitivity, parameter sensitivity, and map-scale expansion, with A*, JPS, PRM, RRT, RRT*, and HG-PRM as baselines. Each scenario uses 50 paired start–goal tasks, and sampling-based methods are repeated with 12 independent random seeds. The results show that LE-HG-PRM provides competitive path quality and structurally regular paths in representative warehouse layouts. Statistical tests further confirm that its path-length advantage is scenario-dependent but significant in several structured and bottleneck-constrained settings. The findings suggest that incorporating explicit warehouse-structure priors can improve roadmap-based global planning for intelligent logistics, while future work should validate the method in Gazebo and physical AGV/AMR platforms. Full article

(This article belongs to the Special Issue Embodied AI for Soft and Bio-Inspired Robotics)

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20 pages, 331 KB

Open AccessReview

Nonverbal Auditory Communication for Human–Robot Interaction in Industry 5.0: A Scoping Review

by Tom Schmid, Manja Lohse, Sven Winkelmann and Alexander von Hoffmann

Robotics 2026, 15(7), 121; https://doi.org/10.3390/robotics15070121 - 26 Jun 2026

Abstract

In Industry 5.0 (I5.0), close-proximity human–robot collaboration demands communication beyond conventional alarms and speech. Nonverbal auditory communication offers a complementary modality, yet its role in I5.0 remains unmapped. This scoping review maps nonverbal auditory communication research in I5.0 Human–Robot Interaction (HRI) and compares [...] Read more.

In Industry 5.0 (I5.0), close-proximity human–robot collaboration demands communication beyond conventional alarms and speech. Nonverbal auditory communication offers a complementary modality, yet its role in I5.0 remains unmapped. This scoping review maps nonverbal auditory communication research in I5.0 Human–Robot Interaction (HRI) and compares it with general HRI literature to identify transfer potential and research gaps. Peer-reviewed English-language articles (2023–April 2026) addressing nonverbal sound in HRI contexts were included. Speech, emotion detection, haptic interfaces and non-HRI domains were excluded. A search with two syntaxes across Web of Science, Scopus, IEEE Xplore, ACM and MDPI, supplemented by citation searching, targeted I5.0-specific (Syntax S1) and general HRI auditory literature (Syntax S2). This created two article record sets, n1 and n2. Articles were organized following Arksey and O’Malley’s framework and PRISMA-ScR into four inductively derived clusters: Sonification, Multimodal Feedback Systems, Safety and Frameworks and Concepts. From 782 initial records, 16 (n1) and 32 (n2) articles were included. In I5.0, multimodal feedback dominates: intentionally designed nonverbal sounds improve situational awareness, reduce cognitive workload and increase perceived safety. Compared to n2, which is shaped by social robotics and emotion-driven sound design, five gaps emerge in I5.0: absent emotion-related sound perception research, missing field studies, missing industry-specific sound design frameworks, underutilized sonification for spatial awareness and safety and no unimodal auditory studies under realistic industrial conditions. A dedicated sound design framework operationalizing I5.0 communicative requirements into designable sound parameters is needed, alongside empirical validation under realistic industrial noise conditions. Full article

(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)

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26 pages, 2428 KB

Open AccessArticle

Reconfigurable Mobile Wireless Sensor Network Coordination for Simultaneous Multi-Target Tracking

by Naeimeh Najafizadeh Sari, Yeqi Sang, Goldie Nejat and Beno Benhabib

Robotics 2026, 15(7), 120; https://doi.org/10.3390/robotics15070120 - 25 Jun 2026

Abstract

This paper presents a distributed coordination framework for simultaneous multi-target tracking using a mobile wireless sensor network (MWSN) based on discrete-event-system principles. The proposed framework employs a finite-state-machine architecture, where autonomous mobile sensors sequentially process detection and tracking events. Unlike passive tracking approaches [...] Read more.

This paper presents a distributed coordination framework for simultaneous multi-target tracking using a mobile wireless sensor network (MWSN) based on discrete-event-system principles. The proposed framework employs a finite-state-machine architecture, where autonomous mobile sensors sequentially process detection and tracking events. Unlike passive tracking approaches that react to target loss after it occurs, the proposed strategy implements predictive handover through Extended-Kalman-Filter-based uncertainty propagation. This enables sensors to anticipate target loss and to reposition auxiliary sensors in advance, acquiring targets along their predicted trajectories. A bidding-based allocation mechanism coordinates sensor assignments by evaluating four competing objectives: network preservation, spatial proximity to handover points, temporal mission feasibility, and estimation uncertainty. The proposed framework integrates four components: EKF-convergence-triggered proactive handover, multi-objective competitive bidding, distributed min–max conflict resolution, and fusion-driven proportional navigation. Unlike existing methods, auxiliary sensors navigate using confidence-weighted EKF estimates shared by neighboring sensors rather than their own measurements. An ablation study over ten Monte Carlo trials confirms that each component contributes independently, with EKF-based predictive triggering identified as the dominant performance driver. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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Graphical abstract

37 pages, 2675 KB

Open AccessArticle

Decentralized Shared Actor–Critic Learning for Collision-Aware Small-Team Multi-Robot Coverage

by Abzal E. Kyzyrkanov, Didar Yedilkhan, Saltanat Amirgaliyeva and Sergazy Narynov

Robotics 2026, 15(7), 119; https://doi.org/10.3390/robotics15070119 - 25 Jun 2026

Abstract

This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, [...] Read more.

This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, five, and six agents are evaluated in an obstacle-free environment using five independent training seeds. In the final training window, the full reward configuration achieved full-team success rates of 98.2 ± 2.9% for four agents, 85.1 ± 18.0% for five agents, and 96.3 ± 2.0% for six agents, with mean landmark coverage above 96% in all cases. The lower mean in the five-agent setting was associated with higher seed-level variability dominated by one low-success seed. Reward ablations without assignment shaping or collision penalties remained viable, and seed-level tests did not show a statistically significant final-window advantage of the full reward configuration. The full configuration reached the 80% rolling-success threshold earlier in median terms, with the clearest seed-level support in the four-agent setting. Within-environment comparison showed higher full-team success than MADDPG and MAPPO under the matched training horizon and final-window protocol. Deterministic arena-size transfer from

15 \times 15

to

30 \times 30

showed decreasing full-team success as arena size increased, while partial landmark coverage remained higher than strict full-team completion. The results support the method for small homogeneous teams in the tested obstacle-free simulation, while larger teams, external obstacles, aerial-robot dynamics, formal safety guarantees, and hardware deployment remain future work. Full article

(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)

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17 pages, 15730 KB

Open AccessArticle

Gynecological Tendon-Driven Continuum Robots: Design and Experimentation

by Clara G. Kierbel and Matteo Russo

Robotics 2026, 15(7), 118; https://doi.org/10.3390/robotics15070118 - 25 Jun 2026

Abstract

Most gynecological interventions do not take advantage of the possible access through the natural orifice to the operating zone and/or use rigid tools, which leads to more invasive procedures. The purpose of this research is to reduce invasiveness by creating a natural orifice [...] Read more.

Most gynecological interventions do not take advantage of the possible access through the natural orifice to the operating zone and/or use rigid tools, which leads to more invasive procedures. The purpose of this research is to reduce invasiveness by creating a natural orifice endoscopic surgical tool. By analyzing the varied anatomies present in patients to extract functional requirements, we propose a conceptual design that allows for better navigation of the environment thanks to a custom design with active control over endoscope shape. We manufactured and tested this new design of a tendon-driven continuum robot in a phantom that is representative of the geometrical properties and variability of a uterus, validating its operation and functionality. Full article

(This article belongs to the Special Issue Selected Papers from MESROB2025: 9th IFToMM International Workshop on New Trends in Medical and Service Robotics)

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5 pages, 154 KB

Open AccessEditorial

Applications of Neural Networks in Robot Control

by Luca Patanè and Paolo Arena

Robotics 2026, 15(7), 117; https://doi.org/10.3390/robotics15070117 - 23 Jun 2026

Abstract

This Editorial introduces the Special Issue “Neural Networks for Robot Control”, which gathers contributions that reflect the rapidly growing intersection of machine learning and robotics [...] Full article

(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)

18 pages, 13691 KB

Open AccessArticle

Numerical and Experimental Validation of an Autonomous Navigation and Mapping Framework for Mobile Robotics

by Antonin Aufrere, Lorenzo Scalera, Eleonora Maset and Alessandro Gasparetto

Robotics 2026, 15(6), 116; https://doi.org/10.3390/robotics15060116 - 16 Jun 2026

Abstract

Autonomous navigation in agricultural environments remains a key challenge for the deployment of mobile robots in precision viticulture. In this paper, we present the numerical and experimental validation of a LiDAR–inertial navigation and mapping framework for mobile robots operating in vineyard-like scenarios. A [...] Read more.

Autonomous navigation in agricultural environments remains a key challenge for the deployment of mobile robots in precision viticulture. In this paper, we present the numerical and experimental validation of a LiDAR–inertial navigation and mapping framework for mobile robots operating in vineyard-like scenarios. A realistic vineyard simulation environment reproducing the geometric structure of vine rows is first developed to evaluate the performance of the proposed framework, considering multiple metrics including mapping time, speed stability, path tracking error, and point cloud reconstruction density. Then, the proposed approach is tested in a real vineyard using a Scout 2.0 mobile robot. Numerical and experimental results demonstrate the feasibility of the navigation and mapping strategy and its robustness during extensive repeated tests in the field. Full article

(This article belongs to the Special Issue IFToMM for Sustainable Development Goals: Contributions from I4SDG 2025 Conference)

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42 pages, 5537 KB

Open AccessArticle

Improved Multi-Objective Cuckoo-Catfish Optimizer for Smooth and Collision-Free Mobile Robot Path Planning

by Jaafar Ahmed Abdulsaheb and Mohanad Azeez Joodi

Robotics 2026, 15(6), 115; https://doi.org/10.3390/robotics15060115 - 15 Jun 2026

Abstract

In this paper, an Adaptive Improved Cuckoo-Catfish Optimizer (AICCO) is proposed for smooth and collision-free mobile robot path planning in static and dynamic environments. The proposed AICCO enhances the original Cuckoo-Catfish Optimizer (CCO) by integrating chaotic opposition-based initialization, nonlinear adaptive control, elite-guided search, [...] Read more.

In this paper, an Adaptive Improved Cuckoo-Catfish Optimizer (AICCO) is proposed for smooth and collision-free mobile robot path planning in static and dynamic environments. The proposed AICCO enhances the original Cuckoo-Catfish Optimizer (CCO) by integrating chaotic opposition-based initialization, nonlinear adaptive control, elite-guided search, strong elite preservation, memetic local refinement, and stagnation-based opposition repair. A weighted-sum scalarized fitness function is formulated to minimize path length and turning-angle variation while maximizing obstacle clearance. The proposed method is evaluated using 23 benchmark functions and further validated in static, dynamic, and nonlinear/reactive obstacle navigation scenarios. The benchmark results show that AICCO achieves the best overall rank among the optimizers compared. In the static planning scenario, the method achieves zero collision penalty and a minimum clearance of 0.785565. In the dynamic online replanning scenario, it achieves zero collisions with a minimum clearance of 1.514250. An additional nonlinear/reactive dynamic scenario further demonstrates that the proposed method can maintain collision-free navigation under more complex obstacle motion. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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36 pages, 21694 KB

Open AccessArticle

Physics-Based Hybrid Control of Mobile Robot Drives with Adaptive Neural Network Compensation

by Alina Fazylova, Kuanysh Alipbayev, Teodor Iliev, Fariza Oraz and Kenzhebek Myrzabekov

Robotics 2026, 15(6), 114; https://doi.org/10.3390/robotics15060114 - 15 Jun 2026

Abstract

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured [...] Read more.

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured injection of neural network correction directly into the physical drive model with a controlled Lyapunov-based adaptation constraint. A mathematical model of the electromechanical drive of a differential mobile platform is developed, taking into account electrical and mechanical dynamics, wheel-to-surface contact interaction, and the system’s energy characteristics. Numerical simulation results demonstrate that the hybrid approach improves tracking accuracy, improves transient response, and ensures stable operation of the control system under parametric uncertainty, adhesion changes, and external disturbances. The proposed architecture maintains the physical interpretability of the model while simultaneously enhancing the system’s adaptability. The obtained results confirm the effectiveness of the developed method and its potential for application in control systems for mobile robotic platforms. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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28 pages, 883 KB

Open AccessArticle

A Dynamic Motion Planner for Trajectory Tracking in HRC

by Timo Habersang, Michael Miro, Victor Caldas, Raza Saeed, Tadele Belay Tuli, Martin Manns and Bernd Kuhlenkötter

Robotics 2026, 15(6), 113; https://doi.org/10.3390/robotics15060113 - 7 Jun 2026

Abstract

In human-robot collaboration (HRC), robots operate alongside humans within a shared workspace. During collaborative handling tasks, human movements are often highly individual and variable. To ensure smooth collaboration, the robot must adapt its trajectory to align with the motion of the human co-worker. [...] Read more.

In human-robot collaboration (HRC), robots operate alongside humans within a shared workspace. During collaborative handling tasks, human movements are often highly individual and variable. To ensure smooth collaboration, the robot must adapt its trajectory to align with the motion of the human co-worker. Therefore, this work proposes a dynamic motion planner that enables the robot to track a dynamically changing reference trajectory. The motion planner is evaluated based on its ability to track the trajectory while respecting joint velocity and acceleration limits and avoiding kinematic singularities. When these constraints are at risk of being violated, the robot temporarily assumes a dominant role and attempts to approximate the reference trajectory as closely as possible. An evaluation using a KUKA iiwa in a laboratory setup demonstrates that the proposed motion planner can effectively track dynamically changing, physically feasible reference trajectories. Assuming that the reference trajectory can be human motion, the motion planner can harmonize human and robot movements during collaborative handling tasks. Full article

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Robotics

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