Robotics

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

Viewed by 48

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

Viewed by 116

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

Viewed by 128

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

Viewed by 774

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

Viewed by 254

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

Viewed by 479

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

Viewed by 434

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

Viewed by 556

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

Viewed by 400

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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21 pages, 4127 KB

Open AccessArticle

Acceptance of an Adaptive Robotic Nursing Assistant for Ambulation Tasks

by Irina Kondaurova, Payman Sharafian, Riten Mitra, Madan M. Rayguru, Bryan D. Edwards, Jeremy Gaskins, Nancy Zhang, Marjorie A. Erdmann, Hyejin Yu, Mimia Cynthia Logsdon and Dan O. Popa

Robotics 2025, 14(9), 121; https://doi.org/10.3390/robotics14090121 - 31 Aug 2025

Viewed by 476

Abstract

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven [...] Read more.

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven by tactile cues from the patient through a force–torque-measuring handlebar, whose signals are fed into a neuro-adaptive controller to achieve a specific admittance behavior regardless of patient strength, weight, or floor incline. Ambulation tasks used two fall-prevention devices: a gait belt and a full-body harness. The attitude toward the robot included perceived satisfaction, usefulness, and assistance, replacing the perceived ease-of-use construct found in the standard technology acceptance model. The effects of external demographic variables on those constructs were also analyzed. The modified technology acceptance model was validated with the simultaneous estimation of the effects of perceived usefulness and assistance on satisfaction. Our analysis employed an integrated hierarchical linear mixed-effects regression model to analyze the complex relationships between model variables. Our results suggest that nursing students rated the ARNA’s performance higher across all model constructs compared to a human assistant. Furthermore, male subjects rated the perceived usefulness of the robot higher than female subjects. Full article

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

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20 pages, 3007 KB

Open AccessArticle

Co-Simulation Model of an Autonomous Driving Rover for Agricultural Applications

by Salvatore Martelli, Valerio Martini, Francesco Mocera and Aurelio Soma’

Robotics 2025, 14(9), 120; https://doi.org/10.3390/robotics14090120 - 29 Aug 2025

Viewed by 529

Abstract

The implementation of autonomous rovers in agriculture could be a promising solution to ensure, at the same time, productivity and sustainability. One of the key points of this kind of vehicle concerns their autonomous driving strategy. Generally, the strategy should include the path [...] Read more.

The implementation of autonomous rovers in agriculture could be a promising solution to ensure, at the same time, productivity and sustainability. One of the key points of this kind of vehicle concerns their autonomous driving strategy. Generally, the strategy should include the path planning and path following algorithms. In this paper, an autonomous driving strategy assessing both is presented. To evaluate the effectiveness of this strategy, a case study of an agricultural rover is presented. A co-simulation model, including a multibody model of the rover, is developed in Matlab/Simulink R2021b and Hexagon Adams 2024 environments to virtually test the rover capabilities and the effects of its dynamics on the robustness of the algorithm. Given different orchard configurations, common but critical work scenarios are investigated, namely a 180° turn and an obstacle avoidance manoeuvre. The actual trajectory obtained during simulations are compared to the ideal trajectory defined in the path planning stage. Furthermore, the torque demand at the electric motors is evaluated. To consider a wide range of possible operating conditions, additional tests with different terrains, payloads and road slopes are included. Results showed that the rover managed to accomplish the considered manoeuvres on loam soil with a maximum trajectory deviation of 0.58 m, but a temporary overload of the motors is needed. On the contrary, in case of difficult terrains, such as muddy soil, the rover was not able to perform the manoeuvre. To limit tire slip, a traction control algorithm is developed and implemented, and the results are compared with the case without control. Full article

(This article belongs to the Special Issue Smart Agriculture with AI and Robotics)

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24 pages, 4130 KB

Open AccessArticle

Experimental Comparative Analysis of Centralized vs. Decentralized Coordination of Aerial–Ground Robotic Teams for Agricultural Operations

by Dimitris Katikaridis, Lefteris Benos, Patrizia Busato, Dimitrios Kateris, Elpiniki Papageorgiou, George Karras and Dionysis Bochtis

Robotics 2025, 14(9), 119; https://doi.org/10.3390/robotics14090119 - 28 Aug 2025

Viewed by 555

Abstract

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication [...] Read more.

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions. Full article

(This article belongs to the Special Issue Smart Agriculture with AI and Robotics)

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38 pages, 19489 KB

Open AccessArticle

Dynamic Space Debris Removal via Deep Feature Extraction and Trajectory Prediction in Robotic Systems

by Zhuyan Zhang, Deli Zhang and Barmak Honarvar Shakibaei Asli

Robotics 2025, 14(9), 118; https://doi.org/10.3390/robotics14090118 - 28 Aug 2025

Viewed by 491

Abstract

This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated [...] Read more.

This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated through a calibrated binocular vision system coupled with a physics-based collision model. Smooth interception trajectories are generated via a particle swarm optimization strategy integrated with a 5–5–5 polynomial interpolation scheme, enabling continuous and time-optimal end-effector motions. To anticipate future arm movements, a Transformer-based sequence predictor is enhanced by replacing conventional multilayer perceptrons with Kolmogorov–Arnold networks (KANs), improving both parameter efficiency and interpretability. In practice, the Transformer+KAN model compensates the manipulator’s trajectory planner to adapt to more complex scenarios. Each component is then evaluated separately in simulation, demonstrating stable tracking performance, precise trajectory execution, and robust motion prediction for intelligent on-orbit servicing. Full article

(This article belongs to the Section AI in Robotics)

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29 pages, 10074 KB

Open AccessArticle

Framework for LLM-Enabled Construction Robot Task Planning: Knowledge Base Preparation and Robot–LLM Dialogue for Interior Wall Painting

by Kyungki Kim, Prashnna Ghimire and Pei-Chi Huang

Robotics 2025, 14(9), 117; https://doi.org/10.3390/robotics14090117 - 27 Aug 2025

Viewed by 1007

Abstract

Task planning for a construction robot requires systematically integrating diverse elements, such as building components, construction processes, user input, and robot software. Conventional robot programming complicates this by requiring precise entity naming, relationship definitions, unstructured language interpretation, and accurate action selection. Existing research [...] Read more.

Task planning for a construction robot requires systematically integrating diverse elements, such as building components, construction processes, user input, and robot software. Conventional robot programming complicates this by requiring precise entity naming, relationship definitions, unstructured language interpretation, and accurate action selection. Existing research has focused on isolated components, such as natural language processing, hardcoded data linkages, or BIM data extraction. We introduce a novel framework using an LLM as the cognitive core for autonomous construction robots, encompassing both data preparation and task planning phases. Leveraging OpenAI’s ChatGPT-4, we demonstrate how LLMs can process structured BIM data and unstructured human inputs to generate robot instructions. A prototype tested in a simulated environment with a mobile painting robot adaptively executed tasks through real-time dialogues with ChatGPT-4, reducing reliance on hardcoded logic. Results suggest that LLMs can serve as the cognitive core for construction robots, with potential for extension to more complex operations. Full article

(This article belongs to the Section AI in Robotics)

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19 pages, 3835 KB

Open AccessReview

A Review on Design, Modeling and Control Technology of Cable-Driven Parallel Robots

by Runze Wang, Jinrun Li and Yangmin Li

Robotics 2025, 14(9), 116; https://doi.org/10.3390/robotics14090116 - 25 Aug 2025

Viewed by 879

Abstract

In view of the limitations of traditional rigid joint robots, cable-driven parallel robots (CDPRs) have shown significant advantages such as wide working space and high payload-to-weight ratio by replacing rigid connectors with flexible cables. Therefore, CDPRs have received widespread attention in the academic [...] Read more.

In view of the limitations of traditional rigid joint robots, cable-driven parallel robots (CDPRs) have shown significant advantages such as wide working space and high payload-to-weight ratio by replacing rigid connectors with flexible cables. Therefore, CDPRs have received widespread attention in the academic community in recent years and been applied to many fields. This review systematically reviews and categorizes the research progress in related fields in the past decade, focusing on mechanical structure design, mainstream mathematical models, and typical planning and control algorithms. In terms of mechanical structure, the advantages and disadvantages of three types of mainstream configurations and their application scenarios are summarized in detail. As for mathematical models, the dynamic modeling methods and various disturbance compensation models are mainly sorted out, and their action mechanisms and inherent limitations are explained. In terms of planning and control, four main research directions are discussed in detail, and their core ideas, evolution context, and development prospects are deeply analyzed. Although significant results have been achieved in the field of CDPR research, it is still necessary to continue to explore the direction of configuration diversification and intelligent autonomy in the future. Full article

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

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13 pages, 603 KB

Open AccessArticle

A Chain Rule-Based Generalized Framework for Efficient Dynamic Analysis of Complex Robotic Systems

by Takashi Kusaka and Takayuki Tanaka

Robotics 2025, 14(9), 115; https://doi.org/10.3390/robotics14090115 - 25 Aug 2025

Viewed by 421

Abstract

System representation via computational graphs has become a cornerstone of modern machine learning, underpinning the gradient-based training of complex models. We have previously introduced the Partial Lagrangian Method—a divide-and-conquer approach that decomposes the Lagrangian into link-wise components—to derive and evaluate the equations of [...] Read more.

System representation via computational graphs has become a cornerstone of modern machine learning, underpinning the gradient-based training of complex models. We have previously introduced the Partial Lagrangian Method—a divide-and-conquer approach that decomposes the Lagrangian into link-wise components—to derive and evaluate the equations of motion for robot systems with dynamically changing structures. That method leverages the symbolic expressiveness of computational graphs with automatic differentiation to streamline dynamic analysis. In this paper, we advance this framework by establishing a principled way to encode time-dependent differential equations as computational graphs. Our approach, which augments the state vector and applies the chain rule, constructs fully time-independent graphs directly from the Lagrangian, eliminating the erroneous time-derivative embeddings that previously required manual correction. Because our transformation is derived from first principles, it guarantees graph correctness and generalizes to any system governed by variational dynamics. We validate the method on a simple serial-link robotic arm, showing that it faithfully reproduces the standard equations of motion without graph failure. Furthermore, by compactly representing state variables, the resulting computational graph achieves a seven-fold reduction in evaluation time compared to our prior implementation. The proposed framework thus offers a more intuitive, scalable, and efficient design and analysis of complex dynamic systems. Full article

(This article belongs to the Section AI in Robotics)

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