Robotics

23 pages, 16253 KB

Open AccessArticle

Preliminary Validation of Nitinol Rod Driven Discrete Continuum Robot for Transoral Surgery by Planar Path Planning with CT Images

by Yeoun-Jae Kim, Ji Eun Oh and Daehan Wi

Robotics 2025, 14(10), 140; https://doi.org/10.3390/robotics14100140 - 30 Sep 2025

Abstract

A Nitinol rod-driven discrete continuum robot with two sections and eight units was developed to support clinicians in performing transoral surgery. The robot measures 120 mm in length, with each unit having a diameter of 15 mm and a height of 20 mm. [...] Read more.

A Nitinol rod-driven discrete continuum robot with two sections and eight units was developed to support clinicians in performing transoral surgery. The robot measures 120 mm in length, with each unit having a diameter of 15 mm and a height of 20 mm. The distal and proximal sections are designed to bend independently, each with two degrees of freedom (DOF) actuated by four Nitinol rods. To validate the independent controllability of the two sections, two-dimensional bending tests and ANSYS simulations were conducted. For the assessment of clinical feasibility, head and neck CT images from ten patients were manually segmented to reconstruct three-dimensional oral cavity models. Ten fictitious reference passages were generated from the lips to the oropharynx, and planar path-planning simulations were performed using these passages. Verification experiments were carried out on three reference passages employing experimentally derived inverse kinematics. The simulation results demonstrated an average reference path-following error within a root mean square (RMS) of 1.9705 mm at maximum insertion length. Experimental path-planning results showed average absolute angular differences of 5.6 degrees in the distal section and 4.1 degrees in the proximal section when compared with the simulations. Full article

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

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21 pages, 3122 KB

Open AccessArticle

TGPSO: An Adaptive Gait Optimization Algorithm for Hexapod Robots in Multi-Terrain Environments

by Guiqiang Bai, Weixu Chen, Jingang Du, Yang Liu, Yanting Luo and Hongde Qin

Robotics 2025, 14(10), 139; https://doi.org/10.3390/robotics14100139 - 30 Sep 2025

Abstract

To address the limited adaptability of traditional fixed-parameter strategies for hexapod robots operating in multi-material terrain environments, this study proposes a terrain-aware gait optimization method based on an improved particle swarm optimization algorithm that incorporates foot-end sinking perception. This method establishes a ground [...] Read more.

To address the limited adaptability of traditional fixed-parameter strategies for hexapod robots operating in multi-material terrain environments, this study proposes a terrain-aware gait optimization method based on an improved particle swarm optimization algorithm that incorporates foot-end sinking perception. This method establishes a ground sinking detection mechanism based on foot-end position sensors, constructs a dynamic weight allocation strategy based on ground bearing capacity, and develops a Terrain-aware Ground Particle Swarm Optimization algorithm (TGPSO) that integrates Latin hypercube sampling, linearly decreasing inertia weights, and stagnation exploration mechanisms. Furthermore, it establishes a unified terrain-based reward function framework to achieve dynamic adjustment of weights for velocity, stability, and transportation efficiency. Simulink simulation verification demonstrates that TGPSO achieves superior optimization performance compared to traditional strategies across three typical terrain types, while exhibiting faster convergence speed and enhanced stability. The research findings provide theoretical foundations and technical support for intelligent motion control of hexapod robots across varying material properties, achieving targeted optimization of locomotion performance under diverse terrain conditions. Full article

(This article belongs to the Section AI in Robotics)

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16 pages, 4945 KB

Open AccessArticle

Research on Energy Consumption Optimization Strategies of Robot Joints Based on NSGA-II and Energy Consumption Mapping

by Dong Yang, Xin Wei and Ming Han

Robotics 2025, 14(10), 138; https://doi.org/10.3390/robotics14100138 - 29 Sep 2025

Abstract

Robot energy consumption is a prominent challenge in intelligent manufacturing and construction. Reducing energy consumption during robot trajectory execution is an urgent issue requiring immediate attention. In view of the shortcomings of traditional trajectory optimization methods, this paper proposes a multi-objective trajectory optimization [...] Read more.

Robot energy consumption is a prominent challenge in intelligent manufacturing and construction. Reducing energy consumption during robot trajectory execution is an urgent issue requiring immediate attention. In view of the shortcomings of traditional trajectory optimization methods, this paper proposes a multi-objective trajectory optimization method that combines energy consumption mapping with the NSGA-II, aiming to reduce robots’ trajectory energy consumption and optimize execution efficiency. By establishing a dynamic energy consumption model, energy consumption mapping is employed to constrain energy consumption within the robot’s workspace, thereby providing guidance for the optimization process. Simultaneously, with energy consumption minimization and time consumption as optimization objectives, the NSGA-II is utilized to obtain the Pareto-optimal solution set through non-dominated sorting and congestion distance calculation. Energy consumption mapping serves as a dynamic feedback mechanism during the optimization process, guiding the distribution of trajectory points towards low-energy-consumption regions, accelerating algorithm convergence, and enhancing the quality of the solution set. The experimental results demonstrate that the proposed method can significantly reduce robots’ trajectory energy consumption and achieve an effective balance between energy consumption and time consumption. Compared with the conventional NSGA-II normalized weighted function method in similar task scenarios, the robot can save 14.87% and 10.47% of its energy consumption, respectively. Compared with traditional methods, this method exhibits superior energy-saving performance and adaptability in complex task environments, providing a novel solution for the efficient trajectory planning of robots. Full article

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

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17 pages, 26449 KB

Open AccessArticle

Federated Learning for Distributed Multi-Robotic Arm Trajectory Optimization

by Fazal Khan and Zhuo Meng

Robotics 2025, 14(10), 137; https://doi.org/10.3390/robotics14100137 - 29 Sep 2025

Abstract

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and [...] Read more.

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article

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

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24 pages, 1542 KB

Open AccessArticle

Investigating Learning Assistance by Demonstration for Robotic Wheelchairs: A Simulation Approach

by Vinícius Barbosa Schettino, Murillo Ferreira dos Santos and Paolo Mercorelli

Robotics 2025, 14(10), 136; https://doi.org/10.3390/robotics14100136 - 28 Sep 2025

Abstract

A major challenge for robots that provide physical assistance is adapting to the needs of different people. To overcome this, personalised assistive models can be created by observing the demonstrations of help provided by an assistant, a setting known as Learning Assistance by [...] Read more.

A major challenge for robots that provide physical assistance is adapting to the needs of different people. To overcome this, personalised assistive models can be created by observing the demonstrations of help provided by an assistant, a setting known as Learning Assistance by Demonstration (LAD). In this work, the case of robotic wheelchairs and drivers with hand control disabilities, which make navigation more challenging, was considered. To better understand LAD and its features, a simulator capable of generating repeatable examples of the triadic interactions between drivers, robots, and assistants was developed. The software is designed to be modular and parametrisable, enabling customisation and experimentation with various synthetic disabilities and scenarios. This approach was employed to design more effective data collection procedures and to enhance learning models. With these, it is shown that, at least in simulation, LAD can be used as follows: for different disabilities; to help consistently; to generalise to physically different environments; and to create customised assistive policies. In summary, the results provide further evidence that LAD is a viable approach for efficiently creating personalised assistive solutions for robotic wheelchairs. Full article

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

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21 pages, 27803 KB

Open AccessArticle

Improving Rover Path Planning in Challenging Terrains: A Comparative Study of RRT-Based Algorithms

by Sarah Swinton, Euan McGookin and Douglas Thomson

Robotics 2025, 14(10), 135; https://doi.org/10.3390/robotics14100135 - 26 Sep 2025

Abstract

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely [...] Read more.

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions. Full article

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

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28 pages, 10315 KB

Open AccessArticle

DKB-SLAM: Dynamic RGB-D Visual SLAM with Efficient Keyframe Selection and Local Bundle Adjustment

by Qian Sun, Ziqiang Xu, Yibing Li, Yidan Zhang and Fang Ye

Robotics 2025, 14(10), 134; https://doi.org/10.3390/robotics14100134 - 25 Sep 2025

Abstract

Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder [...] Read more.

Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder their practical deployment on robotic platforms. To address these challenges, we propose DKB-SLAM, a real-time RGB-D visual SLAM system specifically designed to enhance robotic autonomy in complex dynamic scenes. DKB-SLAM integrates optical flow with Gaussian-based depth distribution analysis within YOLO detection frames to efficiently filter dynamic points, crucial for maintaining accurate pose estimates for the robot. An adaptive keyframe selection strategy balances map density and information integrity using a sliding window, considering the robot’s motion dynamics through parallax, visibility, and matching quality. Furthermore, a heterogeneously weighted local bundle adjustment (BA) method leverages map point geometry, assigning higher weights to stable edge points to refine the robot’s trajectory. Evaluations on the TUM RGB-D benchmark and, crucially, on a mobile robot platform in real-world dynamic scenarios, demonstrate that DKB-SLAM outperforms state-of-the-art methods, providing a robust and efficient solution for high-precision robot localization and mapping in dynamic environments. Full article

(This article belongs to the Special Issue SLAM and Adaptive Navigation for Robotics)

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18 pages, 10386 KB

Open AccessArticle

Mixed-Reality (MR) Enhanced Human–Robot Collaboration: Communicating Robot Intentions to Humans

by Kaiyuan Zhang, Yuchen Yan and Yunyi Jia

Robotics 2025, 14(10), 133; https://doi.org/10.3390/robotics14100133 - 24 Sep 2025

Abstract

Advancements in collaborative robotics have significantly enhanced the potential for human–robot collaboration in manufacturing. To achieve efficient and user-friendly collaboration, prior research has predominantly focused on the robot’s perspective, including aspects such as planning, control, and adaptation. A key approach in this domain [...] Read more.

Advancements in collaborative robotics have significantly enhanced the potential for human–robot collaboration in manufacturing. To achieve efficient and user-friendly collaboration, prior research has predominantly focused on the robot’s perspective, including aspects such as planning, control, and adaptation. A key approach in this domain has been the recognition of human intentions to inform robot actions. However, true collaboration necessitates bidirectional communication, where both human and robot are aware of each other’s intentions. A lack of transparency in robot actions can lead to discomfort, reduced safety, and inefficiencies in the collaborative process. This study investigates the communication of robot intentions to human operators through mixed reality (MR) and evaluates its impact on human–robot collaboration. A laboratory-based physical human–robot assembly framework is developed, integrating multiple MR-based intention communication strategies. Experimental evaluations are conducted to assess the effectiveness of these strategies. The results demonstrate that conveying robot intentions via MR enhances work efficiency, trust, and user comfort in human–robot collaborative manufacturing. Furthermore, a comparative analysis of different MR-based communication designs provides insights into the optimal approaches for improving collaboration quality. Full article

(This article belongs to the Section Humanoid and Human Robotics)

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31 pages, 3516 KB

Open AccessReview

Design, Control, and Applications of Granular Jamming Grippers in Soft Robotics

by J. Cortes and C. Miranda

Robotics 2025, 14(10), 132; https://doi.org/10.3390/robotics14100132 - 24 Sep 2025

Abstract

Granular jamming grippers have emerged as a versatile solution in soft robotics due to their ability to manipulate objects of various shapes and sizes, earning them the label of “universal grippers”. They are composed of granular material confined within an elastic membrane that [...] Read more.

Granular jamming grippers have emerged as a versatile solution in soft robotics due to their ability to manipulate objects of various shapes and sizes, earning them the label of “universal grippers”. They are composed of granular material confined within an elastic membrane that conforms to the object like a fluid and solidifies upon vacuum application, enabling a firm grip through friction and grain interlocking. This work provides a systematic review of the state of the art, addressing their physical principles, the influence of grain and membrane properties, performance characterization methods, and applications across diverse fields. Additionally, the main control variables of these grippers closely related to state variables used in control systems are discussed, along with the current knowledge gaps. Finally, five potential directions for future research are proposed. Full article

(This article belongs to the Special Issue Dynamic Modeling and Model-Based Control of Soft Robots)

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15 pages, 21804 KB

Open AccessArticle

Automated On-Tree Detection and Size Estimation of Pomegranates by a Farmer Robot

by Rosa Pia Devanna, Francesco Vicino, Simone Pietro Garofalo, Gaetano Alessandro Vivaldi, Simone Pascuzzi, Giulio Reina and Annalisa Milella

Robotics 2025, 14(10), 131; https://doi.org/10.3390/robotics14100131 - 23 Sep 2025

Abstract

Pomegranate (Punica granatum) fruit size estimation plays a crucial role in orchard management decision-making, especially for fruit quality assessment and yield prediction. Currently, fruit sizing for pomegranates is performed manually using calipers to measure equatorial and polar diameters. These methods rely [...] Read more.

Pomegranate (Punica granatum) fruit size estimation plays a crucial role in orchard management decision-making, especially for fruit quality assessment and yield prediction. Currently, fruit sizing for pomegranates is performed manually using calipers to measure equatorial and polar diameters. These methods rely on human judgment for sample selection, they are labor-intensive, and prone to errors. In this work, a novel framework for automated on-tree detection and sizing of pomegranate fruits by a farmer robot equipped with a consumer-grade RGB-D sensing device is presented. The proposed system features a multi-stage transfer learning approach to segment fruits in RGB images. Segmentation results from each image are projected on the co-located depth image; then, a fruit clustering and modeling algorithm using visual and depth information is implemented for fruit size estimation. Field tests carried out in a commercial orchard are presented for 96 pomegranate fruit samples, showing that the proposed approach allows for accurate fruit size estimation with an average discrepancy with respect to caliper measures of about 1.0 cm on both the polar and equatorial diameter. Full article

(This article belongs to the Section Agricultural and Field Robotics)

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Graphical abstract

17 pages, 3265 KB

Open AccessArticle

Energy-Based Surface Classification for Mobile Robots in Known and Unexplored Terrains

by Alexander Belyaev and Oleg Kushnarev

Robotics 2025, 14(9), 130; https://doi.org/10.3390/robotics14090130 - 21 Sep 2025

Abstract

Mobile robot navigation in diverse environments is challenging due to varying terrain properties. Underlying surface classification improves robot control and navigation in such conditions. This paper presents an adaptive surface classification system using proprioceptive energy consumption data. We introduce an energy coefficient, calculated [...] Read more.

Mobile robot navigation in diverse environments is challenging due to varying terrain properties. Underlying surface classification improves robot control and navigation in such conditions. This paper presents an adaptive surface classification system using proprioceptive energy consumption data. We introduce an energy coefficient, calculated from motor current and velocity, to quantify motion effort. This coefficient’s dependency on motion direction is modeled for known surface types using discrete cosine transform. A probabilistic classifier, enhanced with memory, compares real-time coefficient values against these models to identify known surfaces. A neural network-based detector identifies encounters with previously unknown terrains by recognizing significant deviations from known models. Upon detection, a least squares method identifies the new surface’s model parameters using data gathered from specific motion directions. Experimental results validate the approach, demonstrating high classification accuracy for known surfaces (91%) and robust detection (96.2%) and identification (MAPE < 3%) of unknown surfaces. Full article

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16 pages, 2449 KB

Open AccessArticle

Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches

by Mojtaba A. Khanesar, Aslihan Karaca, Minrui Yan, Samanta Piano and David Branson

Robotics 2025, 14(9), 129; https://doi.org/10.3390/robotics14090129 - 19 Sep 2025

Abstract

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. [...] Read more.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values. Full article

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

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25 pages, 11424 KB

Open AccessArticle

AI-Based Optimization of a Neural Discrete-Time Sliding Mode Controller via Bayesian, Particle Swarm, and Genetic Algorithms

by Carlos E. Castañeda

Robotics 2025, 14(9), 128; https://doi.org/10.3390/robotics14090128 - 19 Sep 2025

Abstract

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair [...] Read more.

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains

(k_{0}^{*}, k_{1}^{*})

over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective

\tilde{J}

constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 (

7.36 \times 10^{- 5}

rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 (

8.18 \times 10^{- 3}

rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints (

7.81 \times 10^{- 5}

rad,

8.39 \times 10^{- 3}

rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

(This article belongs to the Section AI in Robotics)

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36 pages, 1495 KB

Open AccessReview

Decision-Making for Path Planning of Mobile Robots Under Uncertainty: A Review of Belief-Space Planning Simplifications

by Vineetha Malathi, Pramod Sreedharan, Rthuraj P R, Vyshnavi Anil Kumar, Anil Lal Sadasivan, Ganesha Udupa, Liam Pastorelli and Andrea Troppina

Robotics 2025, 14(9), 127; https://doi.org/10.3390/robotics14090127 - 15 Sep 2025

Abstract

Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and [...] Read more.

Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy. Full article

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

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29 pages, 3444 KB

Open AccessArticle

Thunder Dynamics: A C++ Tool for Adaptive Control of Serial Manipulators

by Marco Baracca, Giorgio Simonini, Simone Tolomei, Yuri De Santis, Paolo Rosa Brusin, Stefano Angeli, Marco Gabiccini, Antonio Bicchi and Paolo Salaris

Robotics 2025, 14(9), 126; https://doi.org/10.3390/robotics14090126 - 13 Sep 2025

Abstract

Robust control techniques are crucial for deploying robotic solutions in real applications and handling model uncertainties in robotic manipulators. The inertial parameters are fundamental to implementing control algorithms. While theoretical approaches to compute the system dynamics and the regressor matrix are well-established, they [...] Read more.

Robust control techniques are crucial for deploying robotic solutions in real applications and handling model uncertainties in robotic manipulators. The inertial parameters are fundamental to implementing control algorithms. While theoretical approaches to compute the system dynamics and the regressor matrix are well-established, they are computationally expensive and a practical implementation framework is still lacking. To address this challenge, we developed a new and efficient method to compute the Coriolis matrix based on Christoffel’s symbols. The result forms the basis of Thunder Dynamics, an open-source software package able to create standalone libraries that compute the system kinematics and dynamics for real-time adaptive control implementation. Thunder Dynamics enables users to create and compile user-defined functions on a robot, which can then be used in C++ or Python 3. To test the proposed framework, we implemented a Cartesian adaptive backstepping controller with axis-angle orientation using our tool. We tested the controller on a seven-degrees-of-freedom manipulator in both simulation and real-world scenarios, varying the levels of uncertainties in the inertial parameters. The results demonstrated that Thunder Dynamics is capable of meeting computational constraints given by the control loop frequency of real systems, permitting, for example, the implementation of advanced controls on commercial manipulators. Full article

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

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3 pages, 138 KB

Open AccessEditorial

Special Issue: Robotics and Parallel Kinematic Machines

by Swaminath Venkateswaran

Robotics 2025, 14(9), 125; https://doi.org/10.3390/robotics14090125 - 3 Sep 2025

Abstract

Parallel kinematic machines (PKMs) are a class of robots that have long been recognized for their higher stiffness and high payload-to-weight ratio and precision compared to their serial counterparts [...] Full article

(This article belongs to the Special Issue Robotics and Parallel Kinematic Machines)

17 pages, 1898 KB

Open AccessArticle

A Novel Methodology for Designing Digital Models for Mobile Robots Based on Model-Following Simulation in Virtual Environments

by Brayan Saldarriaga-Mesa, José Varela-Aldás, Flavio Roberti and Juan M. Toibero

Robotics 2025, 14(9), 124; https://doi.org/10.3390/robotics14090124 - 2 Sep 2025

Abstract

Virtual environment simulations have gained great importance in the field of robotics by enabling the validation and optimization of control algorithms before their implementation on real platforms. However, the construction of accurate digital models is limited not only by the lack of detailed [...] Read more.

Virtual environment simulations have gained great importance in the field of robotics by enabling the validation and optimization of control algorithms before their implementation on real platforms. However, the construction of accurate digital models is limited not only by the lack of detailed characterization of the components but also by the uncertainty introduced by the physics engine and the plugins used in the simulation. Unlike other works, which attempted to model each element of the robot in detail and rely on the physics engine to reproduce its behavior, this paper proposes a methodology based on model following. The proposed architecture forces the simulated robot to replicate the dynamics of the real robot without requiring exactly the same physical parameters. The experimental validation was carried out on two unmanned surface vehicle (USV) platforms with different dynamic parameters and, therefore, different responses to excitation signals, demonstrating that the proposed approach enables a drastic reduction in error. In particular, RMSE and MAE were reduced by more than 98%, with

R^{2}

values close to 1.0, demonstrating an almost perfect correspondence between the real and simulated dynamics. Full article

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

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Graphical abstract

15 pages, 1103 KB

Open AccessArticle

Design and Evaluation of a Sound-Driven Robot Quiz System with Fair First-Responder Detection and Gamified Multimodal Feedback

by Rezaul Tutul and Niels Pinkwart

Robotics 2025, 14(9), 123; https://doi.org/10.3390/robotics14090123 - 31 Aug 2025

Abstract

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and [...] Read more.

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and badges. Motivational design followed the Octalysis framework, and the system was evaluated using validated scales from the Technology Acceptance Model (TAM), the Intrinsic Motivation Inventory (IMI), and the Godspeed Questionnaire. An experimental study was conducted with 32 university students comparing the proposed multimodal system combined with sound-driven first quiz responder detection to a sequential turn-taking quiz response with a verbal-only feedback system as a baseline. Results revealed significantly higher scores for the experimental group across perceived usefulness (M = 4.32 vs. 3.05, d = 2.14), perceived ease of use (M = 4.03 vs. 3.17, d = 1.43), behavioral intention (M = 4.24 vs. 3.28, d = 1.62), and motivation (M = 4.48 vs. 3.39, d = 3.11). The sound-based first-responder detection system achieved 97.5% accuracy and was perceived as fair and intuitive. These findings highlight the impact of fairness, motivational feedback, and multimodal interaction on learner engagement. The proposed system offers a scalable model for designing inclusive and engaging educational robots that promote active participation through meaningful and enjoyable interactions. Full article

(This article belongs to the Section Educational Robotics)

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21 pages, 4634 KB

Open AccessArticle

Geometric and Force-Based Strategies for Dual-Mode Planar Manipulation of Deformable Linear Objects

by Zhenjiu Dai and Hongyu Yu

Robotics 2025, 14(9), 122; https://doi.org/10.3390/robotics14090122 - 31 Aug 2025

Abstract

This paper investigates the fundamental challenges in planar manipulation of deformable linear objects (DLOs), where conventional rigid-body pushing and rotation strategies are often inadequate due to complex deformation dynamics. While the robotic manipulation of rigid objects has been extensively explored, the inherent conflict [...] Read more.

This paper investigates the fundamental challenges in planar manipulation of deformable linear objects (DLOs), where conventional rigid-body pushing and rotation strategies are often inadequate due to complex deformation dynamics. While the robotic manipulation of rigid objects has been extensively explored, the inherent conflict between the infinite degree of freedom in DLOs and the limited control points available in a robotic system presents significant obstacles to effective shape maintenance and force regulation. To address these limitations, we proposed a unified systematic framework for two-dimensional DLO manipulation that integrates object shape modeling with constraint force derivation. By leveraging the principles of system energy minimization and Lagrangian mechanics, our method generates gripper trajectories that simultaneously satisfy the requirement of object shape deformation and force constraints. The efficacy of the framework is validated via a dual-mode manipulation of DLOs, comprising (1) pushing with a static contact point, followed by (2) rotation-based surface alignment through continuous changing contact points. Results demonstrate that our approach achieves integrated shape and force regulation within a single computational framework. Full article

(This article belongs to the Special Issue Dynamic Modeling and Model-Based Control of Soft Robots)

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