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A Shallow-Torque Haptic Device for Wrist Postural Guidance: Design and System Evaluation in a Virtual Rehabilitation Task
Development of an Autonomous UAV for Multi-Modal Mapping of Underground Mines
A Collaborative Robotic System for Autonomous Object Handling with Natural User Interaction

Journal Description

Robotics

Robotics is an international, peer-reviewed, open access journal on robotic systems in theory, design, and applications, published monthly online by MDPI. The International Federation for the Promotion of Mechanism and Machine Science (IFToMM) and Robotic Global Surgical Society (TROGSS) are affiliated with Robotics and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23.7 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 3.3 (2024); 5-Year Impact Factor: 3.8 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

31 pages, 24044 KB

Open AccessSystematic Review

A Systematic Literature Review on Intelligent Soft Hand Exoskeleton Robots: Artificial Intelligence-Enabled Personalisation, Adaptation, and Design Considerations

by Seena Joseph, Wai Keung Fung, Tony Punnoose Valayil, Rajan Prasad and Tim Bashford

Robotics 2026, 15(5), 99; https://doi.org/10.3390/robotics15050099 (registering DOI) - 12 May 2026

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for [...] Read more.

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for patient-centred interventions which place less demand on clinicians, especially wearable devices that can enhance hand function. Advances in artificial intelligence have opened new avenues for developing more reliable and adaptive assistive systems. This study presents a systematic literature review, following the PRISMA protocol on the design elements of hand exoskeleton robots, acknowledging the emerging perspectives on AI integration and ethical considerations. The study provides a comprehensive foundation for future research and development in rehabilitation technologies by systematically synthesising the current mechanical architecture, actuation, sensors, material, weight, and cost aspects of soft hand exoskeleton robots for rehabilitation. The results show important patterns and trade-offs in various design dimensions, providing useful information to direct the development of more accessible and efficient rehabilitation solutions in the future. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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28 pages, 7138 KB

Open AccessArticle

Hybrid LLM-Genetic Programming: Supervising and Generating Diverse Behavior Trees for Autonomous Robot Evolution

by Chi Jie Tan, Eiji Hayashi, Abbe Mowshowitz and Way Soong Lim

Robotics 2026, 15(5), 98; https://doi.org/10.3390/robotics15050098 (registering DOI) - 11 May 2026

Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM [...] Read more.

Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM performs holistic population analysis, adaptively regulates mutation rates, and generates targeted BTs to proactively address behavioral gaps in the evolving population. Unlike conventional evolutionary operators, the LLM introduces high-level semantic guidance by seeding underrepresented behavioral archetypes, thereby complementing stochastic genetic variation with structured exploration. The proposed method is evaluated in a Unity-based multi-task robotic simulation environment. Experimental results show that the hybrid approach significantly outperforms baseline GP with standard adaptive mutation, achieving a 71.7% faster emergence of Complete Robots, a 65.2% faster emergence of Excellent Robots, and a 28% increase in behavioral diversity. Notably, the two systems exhibit opposite mutation dynamics: the LLM-guided system progressively reduces mutation rates to promote exploitation, whereas the baseline maintains a high mutation rate. In addition, the LLM generates approximately 40 targeted BTs per run, proactively seeding the population with underrepresented behavioral archetypes. These performance gains are obtained with only a 13% computational overhead. Full article

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

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34 pages, 15156 KB

Open AccessReview

From Cooperative Dual-Arm Manipulators to Cooperative Multi-Arm Manipulators—Where Are We Standing Today?

by Lander Ketelbuters, Bart Engelen, Ivo Dekker and Karel Kellens

Robotics 2026, 15(5), 97; https://doi.org/10.3390/robotics15050097 (registering DOI) - 11 May 2026

This paper highlights the state of the art in Cooperative Dual-Manipulation (CDM) and Cooperative Multi-Manipulation (CMM), comparing advances in modeling, control, planning, sensing, vision, and end-effector technologies. Methods originally established in CDM have been extended or adapted to support higher complexity of CMM. [...] Read more.

This paper highlights the state of the art in Cooperative Dual-Manipulation (CDM) and Cooperative Multi-Manipulation (CMM), comparing advances in modeling, control, planning, sensing, vision, and end-effector technologies. Methods originally established in CDM have been extended or adapted to support higher complexity of CMM. A historical timeline visualizes the steady growth of cooperative manipulation (CM) and the recent acceleration of CMM driven by rising process complexity and the need for more flexible automation strategies. CM is becoming increasingly relevant as industrial processes demand higher payload capacity, larger workspaces, and greater flexibility. In addition, this paper categorizes existing applications by cooperation type and application domain. Here, a clear dominance of simultaneous object manipulation tasks is visible (fixation-fixation). However, fixation-tooling tasks, where one manipulator grasps the product while another performs a tool operation, and tooling-tooling tasks, where multiple manipulators perform tool operations simultaneously, remain significantly underrepresented. A similar imbalance is found for rigid/non-deformable object manipulation and flexible/deformable object manipulation, respectively. Based on this review, several research gaps are identified: (i) reliable flexible object manipulation methods; (ii) CM strategies for disassembly (e.g., battery pack deconstruction); (iii) complexity in control and planning for multi-manipulator systems; (iv) pathways to industrial deployment beyond laboratory demonstrators; and (v) task-specific tooling and end-effector innovation. Full article

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

26 pages, 2940 KB

Open AccessArticle

Attention-Guided Path Planning: Learning Efficient Heuristics for Mobile Robot Navigation via Deep Neural Networks

by Abderrahim Waga, Said Benhlima, Ali Bekri, Fatima Zahrae Saber, Jawad Abdouni, Toufik Mzili and Ahmed Regragui

Robotics 2026, 15(5), 96; https://doi.org/10.3390/robotics15050096 (registering DOI) - 11 May 2026

Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep [...] Read more.

Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep learning-based framework for 2D trajectory prediction in grid environments. The framework employs attention mechanisms specifically designed for path planning tasks. In particular, we design an Attention U-Net architecture that employs attention gates for effective path area focusing and residual connections for efficient feature selection. To validate our method, the Attention U-Net architecture is trained on 5000 randomly sampled 40 × 40 environments and tested on a separate test set of 200 environments. The experimental results show that the Attention U-Net architecture significantly outperforms the A* algorithm. It expands 62% fewer nodes (207.6 vs. 543.11) and achieves near-optimal path lengths (99.8% of optimal) and planning speed (0.78 ms vs. 1.19 ms). Furthermore, the Attention U-Net architecture achieves a 100% success rate for A* path planning with the attention heuristic, demonstrating the effectiveness of the attention heuristic for path planning. Full article

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

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24 pages, 8395 KB

Open AccessArticle

Energy-Aware Multi-Agent Proximal Policy Optimization with Depletion Safety Constraints for Multi-Robot Coordination

by Yassin Abdelmeguid and Ammar Hasan

Robotics 2026, 15(5), 95; https://doi.org/10.3390/robotics15050095 - 8 May 2026

Multi-robot systems operating on battery power face fundamental constraints through which energy limitations directly impact mission success. The existing multi-agent reinforcement learning approaches optimize for task performance without explicit energy consideration, leading to inefficient consumption and depletion risk. This paper presents a framework [...] Read more.

Multi-robot systems operating on battery power face fundamental constraints through which energy limitations directly impact mission success. The existing multi-agent reinforcement learning approaches optimize for task performance without explicit energy consideration, leading to inefficient consumption and depletion risk. This paper presents a framework for energy-aware multi-agent coordination that treats battery management as a safety constraint, rather than an optimization objective. We introduce Energy-Aware Multi-Agent Proximal Policy Optimization (EA-MAPPO) with energy-augmented observations and shaped rewards and extend it to Safe Energy-Aware MAPPO (SEA-MAPPO) combining predictive action masking with safety-oriented reward shaping. An experimental validation on the Georgia Tech Robotarium with 7 agents demonstrates that SEA-MAPPO reaches 95% goal completion 19× faster than standard MAPPO, requiring only 0.5 M environment steps versus 9.4 M. Throughout training, SEA-MAPPO reduces cumulative depletion events by 93% compared to MAPPO while maintaining superior energy efficiency. SEA-MAPPO achieves 100% goal completion versus 81.5% for MAPPO at the same training budget. Physical deployment on GTernal robots without fine-tuning achieves 100% goal completion with zero depletion events across 70 robot-trials, with the energy predictor achieving

R^{2} = 0.89

with measured power consumption. Full article

(This article belongs to the Section AI in Robotics)

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15 pages, 4759 KB

Open AccessArticle

AR-Based Teleoperation of an Omnidirectional Mobile Robot for UV-C Disinfection

by Andres de la Rosa-Garcia, Alma Guadalupe Rodriguez-Ramirez, Beatriz Alvarado Robles, Israel Soto-Marrufo, Diana Ortiz-Muñoz, Victor Manuel Alonso-Mendoza, David Luviano-Cruz and Francesco Garcia-Luna

Robotics 2026, 15(5), 94; https://doi.org/10.3390/robotics15050094 - 1 May 2026

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, [...] Read more.

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces. Full article

(This article belongs to the Special Issue Development of Biomedical Robotics)

1 pages, 126 KB

Open AccessCorrection

Correction: Albustanji et al. Robotics: Five Senses plus One—An Overview. Robotics 2023, 12, 68

by Rand N. Albustanji, Shorouq Elmanaseer and Ahmad A. A. Alkhatib

Robotics 2026, 15(5), 93; https://doi.org/10.3390/robotics15050093 (registering DOI) - 30 Apr 2026

Error in Figure [...] Full article

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

23 pages, 5692 KB

Open AccessArticle

Time-Scaled Coordination and Diffeomorphic Mapping for Fixed-Position Convergence in Smart Transportation Systems

by Luigi D’Alfonso, Alp Merzi and Giuseppe Fedele

Robotics 2026, 15(5), 92; https://doi.org/10.3390/robotics15050092 - 30 Apr 2026

This paper presents a novel distributed coordination framework for multi-agent robotic swarms tailored for smart transportation applications. The proposed approach addresses the critical pre-transportation phase where a fleet of mobile robots, eventually with different sizes, must converge to fixed positions around an object [...] Read more.

This paper presents a novel distributed coordination framework for multi-agent robotic swarms tailored for smart transportation applications. The proposed approach addresses the critical pre-transportation phase where a fleet of mobile robots, eventually with different sizes, must converge to fixed positions around an object to ensure effective caging within a user-defined prescribed time. By leveraging a time-varying diffeomorphic mapping based on an affine transformation, the strategy embeds prescribed-time guarantees within a swarm-inspired framework that maps agents between virtual and real reference frames. This methodology ensures the simultaneous achievement of precise target convergence, finite-time stability regardless of initial conditions, and inherent collision avoidance by explicitly considering the physical footprint of each robotic unit. The control protocol is first derived for scalar systems and subsequently extended to multidimensional robotic fleets using additional diffeomorphism-based techniques, which allow for the management of multiple non-interacting swarms to reduce network communication overhead. Full article

(This article belongs to the Section Aerospace Robotics and Autonomous Systems)

22 pages, 55201 KB

Open AccessArticle

A Distributed and Reconfigurable Architecture for Unified Multimodal Indoor Localization of a Mobile Edge Node in a Cyber-Physical Context

by Theodoros Papafotiou, Emmanouil Tsardoulias and Andreas Symeonidis

Robotics 2026, 15(5), 91; https://doi.org/10.3390/robotics15050091 - 30 Apr 2026

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge [...] Read more.

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems. Full article

(This article belongs to the Special Issue Localization and 3D Mapping of Intelligent Robotics)

31 pages, 7859 KB

Open AccessArticle

Uncertainty-Aware LiDAR–Inertial–Visual SLAM with Adaptive Fusion and Multi-Channel Geometric Loop Closure

by Qixue Zhong, Jing Xing, Jian Liu and Luqing Luo

Robotics 2026, 15(5), 90; https://doi.org/10.3390/robotics15050090 - 29 Apr 2026

Accurate and robust localization and mapping in complex and dynamic environments remain a fundamental challenge for autonomous systems. LiDAR–Inertial–Visual Odometry (LIVO) integrates the complementary strengths of LiDAR geometry, visual appearance, and inertial motion constraints. However, existing LIVO systems still suffer from limited adaptability [...] Read more.

Accurate and robust localization and mapping in complex and dynamic environments remain a fundamental challenge for autonomous systems. LiDAR–Inertial–Visual Odometry (LIVO) integrates the complementary strengths of LiDAR geometry, visual appearance, and inertial motion constraints. However, existing LIVO systems still suffer from limited adaptability to sensor degradation, weak loop-closure robustness, and insufficient cross-modal consistency modeling. This paper presents a robust multi-sensor SLAM framework that integrates an uncertainty-aware LIVO front-end, a geometry-driven loop-closure module, and a cross-modal consistency factor-graph back-end. We develop an uncertainty-aware iterated error-state Kalman filter (iESKF) to tightly fuse LiDAR, visual, and inertial measurements, with measurement covariances dynamically adjusted according to innovation statistics, feature-matching quality, and observability. To improve global consistency, we propose a multi-channel Binary Triangle Constraint (mBTC) descriptor for LiDAR-based loop detection, which enhances robustness under viewpoint changes and appearance degradation. In addition, we introduce a cross-modal consistency factor to explicitly constrain the relative motion agreement between visual and LiDAR odometries. Extensive experiments on multiple public benchmarks demonstrate improved accuracy, loop-closure reliability, and long-term consistency compared with state-of-the-art LIVO systems. Full article

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

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25 pages, 5837 KB

Open AccessArticle

Design of a Cartesian Robot for Pushing Bulk Materials in a Transport and Packaging Container

by Askar Seidakhmet, Amandyk Tuleshov, Assylbek Jomartov, Akmurat Altynbek, Jacek Cieślik, Azizbek Abduraimov, Aziz Kamal, Madi Kaliyev and Zair Ualiyev

Robotics 2026, 15(5), 89; https://doi.org/10.3390/robotics15050089 - 29 Apr 2026

Automating the pushing of bulk materials deposited through the hatch of a transport and packaging container (TPC) requires the development of a specialized robot equipped with an end effector capable of displacing material within a spatially constrained environment. This paper proposes a Cartesian [...] Read more.

Automating the pushing of bulk materials deposited through the hatch of a transport and packaging container (TPC) requires the development of a specialized robot equipped with an end effector capable of displacing material within a spatially constrained environment. This paper proposes a Cartesian robot design featuring a cellular end effector—comprising a grid of rectangular compartments—and a control system that enables the pushing of bulk material batches loaded via the hatch. This work presents experimental results regarding robotic workcell performance as a function of its end effector immersion depth and the material’s moisture content. Finite element modeling (FEM) of the end effector is detailed, determining the resistance forces encountered during movement at various immersion depths within the bulk material. Furthermore, an analysis of the container’s design was conducted to determine the final layer thickness of the bulk material after leveling five consecutive loaded portions. Newton–Euler equations are formulated to describe the movement dynamics of the end effector, considering variables such as immersion depth and material moisture. Additionally, a motion control algorithm was developed to accommodate varying displacements of the conical heap’s apex relative to the hatch center, integrated within the overall Cartesian robot control system. The derived results and recommendations facilitate the effective pushing and redistribution of loaded material batches within the container. Finally, finite element analysis and experimental validation confirm the structural strength and rigidity of the Cartesian robotic workcell, ensuring that the maximum elastic deflection of the end effector under peak dynamic load remains ≈ 1 mm. Full article

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

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62 pages, 10380 KB

Open AccessReview

Semantic SLAM with Multi-Modal Perception: Survey on Robust Long-Term Localization for Autonomous Vehicles

by Álvaro Navarro-Pérez, Bladimir Bacca-Cortés and Eduardo Caicedo-Bravo

Robotics 2026, 15(5), 88; https://doi.org/10.3390/robotics15050088 - 28 Apr 2026

Long-term localization in dynamic and changing environments remains a key challenge for autonomous vehicles. Semantic Simultaneous Localization and Mapping (SLAM) enhances traditional SLAM by integrating high-level semantic understanding, enabling robust mapping and localization even under complex scenarios. In this context, multi-modal sensor fusion—particularly [...] Read more.

Long-term localization in dynamic and changing environments remains a key challenge for autonomous vehicles. Semantic Simultaneous Localization and Mapping (SLAM) enhances traditional SLAM by integrating high-level semantic understanding, enabling robust mapping and localization even under complex scenarios. In this context, multi-modal sensor fusion—particularly the combination of LiDAR and camera data—has proven essential in leveraging complementary strengths: the geometric accuracy of LiDAR and the rich semantic cues from images. A significant advancement in this domain is the adoption of graph-based semantic localization frameworks, where semantic entities and spatial relationships are encoded in graph structures to improve map consistency, loop closure detection, and data association over time. This review presents a comprehensive survey of recent developments in Semantic SLAM, with a focus on long-term localization for autonomous vehicles using multi-modal fusion strategies. We categorize existing methods into traditional SLAM, vision-based, point-cloud-based, and graph-based techniques, emphasizing the role of semantic data association and loop closure in maintaining long-term consistency. Additionally, we discuss the integration of deep learning techniques for semantic segmentation and feature extraction. Finally, we analyze widely used datasets and evaluation metrics, identifying current limitations and proposing directions for future research on robust, scalable, and semantically enriched localization. Full article

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

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27 pages, 2173 KB

Open AccessArticle

Efficient Incremental SLAM via Information-Guided Gating and Selective Partial Optimization

by Reza Arablouei

Robotics 2026, 15(5), 87; https://doi.org/10.3390/robotics15050087 - 27 Apr 2026

We present an efficient incremental SLAM back-end that reduces computation while preserving accuracy close to that of a full incremental Gauss–Newton (GN) solver across benchmark pose-graph datasets. The method combines information-guided gating (IGG), which uses a log-determinant-based information surrogate to decide when broad [...] Read more.

We present an efficient incremental SLAM back-end that reduces computation while preserving accuracy close to that of a full incremental Gauss–Newton (GN) solver across benchmark pose-graph datasets. The method combines information-guided gating (IGG), which uses a log-determinant-based information surrogate to decide when broad updates are warranted, with selective partial optimization (SPO), which confines multi-iteration GN updates to variables that remain affected after each iteration. We provide a local perturbation analysis, showing that, under standard regularity conditions, the proposed approximation tracks full GN within a threshold-controlled neighborhood and recovers the same local minimizer and asymptotic convergence rate when the effective approximation error vanishes asymptotically. Experiments on benchmark pose-graph SLAM datasets show competitive final and increment-averaged accuracy together with substantial reductions in update and solve FLOPs. These results support IGG-SPO as a practically promising SLAM back-end for robots operating under limited onboard computational resources. Full article

(This article belongs to the Special Issue State of the Art in Mobile Robot Localization)

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12 pages, 8586 KB

Open AccessArticle

Photogrammetric Characterization of Robot Positioning Accuracy and Repeatability

by Sebastián Chajón, Jörg Reiff-Stephan and Norman Günther

Robotics 2026, 15(5), 86; https://doi.org/10.3390/robotics15050086 - 27 Apr 2026

Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. [...] Read more.

Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems. Full article

(This article belongs to the Special Issue Advanced Grasping and Motion Control Solutions: 2nd Edition)

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26 pages, 30235 KB

Open AccessArticle

Multi-Stage Parameter Search for Robot Path Planning in Bottom-Up Vat 3D Printing

by Evan Rolland, Ilian A. Bonev, Evan Jones, Pengpeng Zhang, Cheng Sun and Nanzhu Zhao

Robotics 2026, 15(5), 85; https://doi.org/10.3390/robotics15050085 - 26 Apr 2026

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. [...] Read more.

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements. Full article

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

24 pages, 3856 KB

Open AccessArticle

Human–Robot Interaction: External Force Estimation and Variable Admittance Control Incorporating Passivity

by Jun Wan, Zihao Zhou, Nuo Yun, Kehong Wang and Xiaoyong Zhang

Robotics 2026, 15(5), 84; https://doi.org/10.3390/robotics15050084 - 22 Apr 2026

In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their [...] Read more.

In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their reliance on accurate dynamic models. To address these challenges, this paper proposes a sensorless external force estimation and variable admittance control method that models robot dynamic uncertainties and interaction forces as normally distributed stochastic quantities. An improved particle swarm optimization algorithm is introduced to calibrate the variance parameters, enhancing estimation accuracy and robustness. Furthermore, an energy-based variable admittance control strategy is developed, which preserves system passivity by adaptively adjusting inertia and damping gains based on real-time energy variations. The proposed method was validated on a redundant robot platform. Experimental results show that the external force and torque estimation errors remain below 3 N and 3 N.m, respectively, with lower detection delays and errors than those of a first-order generalized momentum observer in collision detection. Variable admittance experiments demonstrate that the system maintains passivity and stable interaction even under sudden arm stiffness changes. The approach is well-suited for industrial applications requiring safe, sensorless, and compliant human–robot collaboration. Full article

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

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15 pages, 4474 KB

Open AccessArticle

A New 3R1T Parallel Robot for Minimally Invasive Surgery: Design, Control and Preliminary Performance Evaluation

by Aislinn McAleenan, Yinglun Jian, Yan Jin, Dan Sun and Johnny Moore

Robotics 2026, 15(5), 83; https://doi.org/10.3390/robotics15050083 - 22 Apr 2026

Minimally invasive surgery (MIS) has transformed modern surgical operations by reducing pain, trauma, scarring and recovery time for the patient. However, precision, stability and accuracy continue to limit surgical performance. Robots can exhibit better precision and stability than humans and have the potential [...] Read more.

Minimally invasive surgery (MIS) has transformed modern surgical operations by reducing pain, trauma, scarring and recovery time for the patient. However, precision, stability and accuracy continue to limit surgical performance. Robots can exhibit better precision and stability than humans and have the potential to improve MIS results. This work presents the design and development of a patented 3R1T parallel robot for MIS. The mechanism incorporates a coaxial spherical parallel architecture enabling three rotational degrees of freedom, combined with a remotely actuated translational fourth degree of freedom, therefore reducing the weight of the moving structure, decreasing inertial forces and increasing the system accuracy. The kinematic design is analyzed to achieve the required workspace, motor torque requirements are calculated, and a control system with integrated inverse kinematics is developed. A prototype was manufactured, and preliminary experiments were conducted to evaluate the orientation repeatability of the robot. Results demonstrated a repeatability of ±22.86 μm, commensurate with typical MIS constraints. This suggests that the proposed robot offers potential improvements in precision and control for minimally invasive surgical procedures, over traditional manual methods. 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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25 pages, 5544 KB

Open AccessArticle

Retrofitting a Legacy Industrial Robot Through Monocular Computer Vision-Based Human-Arm Posture Tracking and 3-DoF Robot-Axis Control (A1–A3)

by Paúl A. Chasi-Pesantez, Eduardo J. Astudillo-Flores, Valeria A. Dueñas-López, Jorge O. Ordoñez-Ordoñez, Eldad Holdengreber and Luis Fernando Guerrero-Vásquez

Robotics 2026, 15(4), 82; https://doi.org/10.3390/robotics15040082 - 21 Apr 2026

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles

θ^{(h)}

and map them to robot-axis joint targets [...] Read more.

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles

θ^{(h)}

and map them to robot-axis joint targets

q_{cmd}^{(r)}

for A1–A3 on a KUKA KR5-2 ARC HW, while keeping the wrist orientation (A4–A6) fixed. Rather than targeting full six-DoF manipulation, the main contribution is an experimental characterization of how far monocular 2D posture-to-axis mapping can be used reliably for coarse placement and safeguarded low-speed demonstrations on a legacy robot platform. Vision-side accuracy was evaluated per axis against goniometer-based reference angles

θ_{ref}^{(h)}

, showing low errors for A2–A3 within the tested range and larger errors for A1 due to monocular yaw/depth ambiguity and occlusions. The study also analyzes failure modes during simultaneous multi-joint motion, where performance degrades notably, especially for A2 and A3, and reports practical mitigation directions such as improved viewpoints, multi-view/depth sensing, and stricter dropout handling. Runtime behavior is additionally characterized through a loop timing budget, with an end-to-end latency of 185.44 ms and an effective loop frequency of 5.39 Hz, which is consistent with low-speed online operation within the demonstrated scope. The system was implemented in a fenced industrial cell with restricted access and emergency stop; no collaborative operation is claimed. Full article

(This article belongs to the Special Issue Artificial Vision Systems for Robotics)

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37 pages, 4888 KB

Open AccessReview

Robotics in Precision Agriculture: Task-, Platform-, and Evaluation-Oriented Review

by Natheer Almtireen and Mutaz Ryalat

Robotics 2026, 15(4), 81; https://doi.org/10.3390/robotics15040081 - 20 Apr 2026

Robotics is increasingly positioned as an enabling technology for precision agriculture, where management actions must be spatially and temporally targeted under constraints on labour, input use, safety, and environmental impact. This review synthesises studies on agricultural field robotics and organises the literature along [...] Read more.

Robotics is increasingly positioned as an enabling technology for precision agriculture, where management actions must be spatially and temporally targeted under constraints on labour, input use, safety, and environmental impact. This review synthesises studies on agricultural field robotics and organises the literature along four complementary axes: task (monitoring, weeding, spraying, and harvesting), platform (UGV, UAV, gantry/fixed-structure, greenhouse robot, and hybrid systems), autonomy-stack module (perception, localisation, planning, control, actuation, safety, and human–robot interaction), and evaluation setting (lab, greenhouse, open-field single season, and open-field multi-season/multi-site). Across these dimensions, this review analyses how platform constraints shape sensing geometry, actuation capability, localisation reliability, energy/endurance, supervision burden, and safety requirements. It further examines enabling technologies that recur across tasks, including vision and multimodal perception under occlusion and illumination variability, localisation and mapping under weak or denied GNSS, uncertainty-aware planning in deformable and partially observed environments, and compliant end-effectors for contact-rich operations. Beyond cataloguing systems, this paper emphasises evaluation practice by synthesising core task-relevant metrics, comparing laboratory and field validation settings, and proposing a reporting checklist and benchmark ladder to improve reproducibility and cross-study comparability. This review identifies recurring bottlenecks in domain shift, long-term autonomy, calibration robustness, crop-safe actuation, and safety assurance near humans, and it concludes with a staged research roadmap linking near-term evaluation reform to longer-term credible multi-site autonomy. Overall, this paper provides a structured framework for interpreting agricultural robotic systems not only by application but also by deployment context, system maturity, and evaluation credibility. Full article

(This article belongs to the Special Issue Perception and AI for Field Robotics)

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25 pages, 4973 KB

Open AccessArticle

LLM-Assisted Plan Execution for Robots in Dynamic Environments

by Juan Diego Peña-Narvaez, Rodrigo Pérez-Rodríguez, Juan Carlos Manzanares, Francisco Miguel Moreno and Francisco Martín-Rico

Robotics 2026, 15(4), 80; https://doi.org/10.3390/robotics15040080 - 15 Apr 2026

In recent years, planning frameworks have enabled the creation and execution of plans in robots using classical planning approaches based on the Planning Domain Definition Language (PDDL). The dynamic nature of the environments in which these robots operate requires that execution plans adapt [...] Read more.

In recent years, planning frameworks have enabled the creation and execution of plans in robots using classical planning approaches based on the Planning Domain Definition Language (PDDL). The dynamic nature of the environments in which these robots operate requires that execution plans adapt to new conditions, either by repairing plans to improve efficiency or because they are no longer valid. Determining the appropriate moment to initiate such repairs is the focus of our research. This paper presents a novel approach to this problem by using Large Language Models (LLMs) to make informed plan repair decisions during robot operation. Our approach introduces an LLM-based semantic evaluation heuristic that goes beyond the traditional heuristic methods employed in symbolic planning frameworks, while addressing the common hallucinations associated with task planning when relying solely on generative artificial intelligence. Our approach uses the semantic evaluation capabilities of LLMs to track environmental features and forecast hazards. This allows the system to proactively identify dangerous situations and adapt plans more efficiently. We experimentally demonstrate the validity of our approach using real robots in environments where both the environmental conditions and the goals to be achieved change dynamically. Full article

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

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