Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research.

When is an unknown, confined environment traversable for a specific ground robot using only touch? We answer by (i) giving an environment-anchored definition of traversability, expressed through the max-min value T★(E;A)=supπ∈ΠS→Ginfs∈[0,1]ϕ(π(s)), where the bottleneck margin ϕ aggregates the clearance, curvature (ρ≥Rmin), slope/step, and friction constraints, and (ii) introducing an on-policy, tactile certificate (TC) that maintains a conservative, monotone lower bound Tt using partial contact histories. The TC fuses pessimistic free-space from contacts and the body envelope, the M3 decaying contact memory as a risk prior, and local bend/FSR proxies; a certificate is issued when Tt>0 and the explored corridor graph connects S to G. Relative to Papers 1–2 (tactile traversal; offline software assurance), this work formalizes traversability itself and provides a tactile-only, online certificate computable during runs. In a retrospective analysis of 660 trials across Indoor/Outdoor/Dark lighting environments, (H1) the early TC margin predicts success and traversal time better than contact/dwell heuristics (higher AUC/R2), (H2) the TC predictivity is lighting-invariant, and (H3) speed-gating M3 by a TC margin recovers part of the CB-V speed gap without degrading success. Artifacts include the TC implementation, explored-corridor graphs, and per-trial TC time series added to the Paper-1 log bundle; these materials are available from the corresponding author upon reasonable request.

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration procedure, and the performance measurements of the experimental model based on finite element analyses of the 3D model, are also detailed in this paper. Based on these finite element analyses, a region of the robot that introduces clearance during the operation of the experimental model is found. The paper also presents the methodology used for mapping the robot’s workspace with an optical system, which enabled improvements to ensure coverage of the entire pancreas area. The results obtained before and after the mechanical improvements are presented, demonstrating a reduction in clearance by up to 4.1 times following part replacement, as well as a workspace extension that enables the active instrument to reach the entire pancreatic region.

This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and intelligent task recovery from failure points. The framework combines island model genetic algorithm-based initial optimization with an event-driven architecture for handling robot failures during mission execution. Our key contribution is the integration of crisis-aware capabilities with the island model paradigm, enabling task resumption from failure points and dynamic reoptimization, while preserving the diversity benefits of multi-population evolution. When a robot fails, the system intelligently substitutes replacement robots and resumes interrupted tasks from their exact failure point, rather than restarting from the beginning. This significantly improves mission efficiency and resilience. We introduce a temporal scheduling mechanism that tracks actual task execution states and calculates remaining work upon failure, enabling true task continuation. Experimental validation across 57 diverse scenarios with 2340 independent runs demonstrates that the island model achieves higher fitness scores, maintains greater population diversity, exhibits more consistent performance, and recovers faster from crisis events compared to the standard single-population genetic algorithm.

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript.

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances.

This paper presents the design, prototype, and experimental evaluation of the AlmatyExoElbow, a lightweight cable-driven robotic exoskeleton that is intended to support elbow joint rehabilitation. The device provides two active degrees of freedom for flexion/extension and pronation/supination. It also incorporates a sensor-based control system for accurate motion tracking. The mechanical structure is fabricated using 3D-printed PLA plastic, resulting in a compact, modular, and comfortable design suitable for prolonged use. The control architecture is based on an Arduino Nano microcontroller integrated with IMU sensors, enabling the real-time monitoring of elbow motion and the precise reproduction of physiologically relevant movement patterns. The results of experimental testing demonstrate smooth and stable operation, confirming reliable torque transmission through antagonistic cable mechanisms. Overall, the proposed design achieves a balanced combination of functionality, portability, and user comfort, highlighting its potential for upper-limb rehabilitation applications in both clinical and home-based settings.

This article presents a methodology for real-time forecasting of a fire-extinguishing agent jet trajectory from a robotic fire monitor under wind influence, which can significantly displace the impact area position and complicate targeting. The proposed methodology is designed for controlling firefighting robots in conditions where visual monitoring of the impact area is impeded by factors such as: obscuration of the fire-extinguishing agent flow by smoke, low visibility of its fragmented particles against the background environment, and long-range jet discharge. Trajectory forecasting is implemented using a neural network model. The training and verification of this model are performed with datasets constructed from the results of numerical simulations of fire-extinguishing agent motion under wind influence, based on Computational Fluid Dynamics (CFD) methods. Experimentally obtained data are used for the validation of the trained neural network model and the selected CFD models. The paper describes the methodology for conducting full-scale tests of fire monitors; a photogrammetric algorithm for generating validation datasets from the test results; an algorithm for calculating target characteristics, which describe the jet trajectory and are consistent with experimental data, used for forming training and verification datasets based on simulation; and a procedure for selecting Computational Fluid Dynamics models and their parameters to ensure the required accuracy. The article also presents the results of an experimental evaluation of the developed methodology’s effectiveness for real-time prediction of the water jet trajectory from a fire monitor under various control and disturbance parameters.

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.

Robots and artificial intelligence have revolutionized the healthcare sector. Patient transportation within hospitals is emerging as a critical application; however, reducing errors and inefficiencies caused by human intervention and ensuring safe, efficient, and reliable movement of patients are necessary. This study provides a comprehensive review of research and technological trends in healthcare robotics, focusing on the design, algorithms, and applications of following robots for patient transportation. We examine foundational robotic algorithms, sensor and navigation technologies, and human–robot interaction techniques that enable robots to safely follow patients and assist medical personnel. Additionally, we survey the evolution of transportation robots across industries and highlight current research on hospital-specific following robots. Finally, we survey the current research and trends in following robots and robots for patient transportation to assist medical personnel in real-world healthcare applications. A comprehensive and in-depth understanding of the design and application of following robots can be pivotal to guiding future research on following robots and laying the foundation for their commercialization in healthcare.