Search Results (2,752)

Collaborative robotics and computer vision systems are increasingly important in automating complex industrial tasks with greater safety and productivity. This work presents an integrated vision system powered by a trained neural network and coupled with a collaborative robot for real-time sorting and quality inspection in a food product conveyor process. Multiple object detection models were trained on custom datasets using advanced augmentation techniques to optimize performance. The proposed system achieved a detection and classification accuracy of 98%, successfully processing more than 600 items with high efficiency and low computational cost. Unlike conventional solutions that rely on ROS (Robot Operating System), this implementation used a Windows-based Python framework for greater accessibility and industrial compatibility. The results demonstrated the reliability and industrial applicability of the solution, offering a scalable and accurate methodology that can be adapted to various industrial applications. Full article

The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology. Full article

The current Bangladeshi dairy sector faces many problems related to sustainability indicators from economic, social, and environmental perspectives. In this circumstance, they must combine cutting-edge innovation to overcome growing sustainability concerns and technical revolutions to become smart farms. This study analyzes how dairy farmers might use cutting-edge technologies in their dairy sub-processes to determine the benefits of achieving additional productivity and efficiency. This paper examines precision livestock farming, information analytics, and alternative energy sources to reduce environmental hazards and increase resource efficiency. Using cutting-edge technologies like artificial intelligence (AI), machine learning (ML), robotics (RPA), Internet of Things (IoT), data analytics, system dynamics, and simulation modeling can assist the farmers in improving the results. Analyzing developing country case studies and best practices reveals crucial answers for reconciling sustainability stewardship and operational efficiency. The system dynamics method builds a simulation model and finds the projected results before implementing it in real life. The findings provide considerable waste reduction and productivity gains through technological deployments. The simulation model creates two scenarios of ‘current’ and ‘technology-adopted’ processes to examine the transformational benefits of sustainable practices. A case study method was adopted for this technology deployment to organize a comprehensive strategy that blends technology and sustainability. This study ends with recommendations for dairy farmers and policymakers to create a resilient and environmentally friendly dairy operation to secure the dairy sector’s long-term viability in transforming technologies. Future farms can follow the practical, technical, and policy, as well as recommendations to improve their processes, such as smart farm concepts available in academia and dairy-developed countries. Full article

Background: Liver resection is the standard treatment for resectable hepatocellular carcinoma (HCC). The advent of robotic surgery has extended its application in liver surgery, reducing post-operative complications without compromising oncological safety. This study is a retrospective series with the aim of analyzing the preoperative patient’s and tumor’s characteristics and evaluating intraoperative and post-operative data in terms of hospital stay, complications, and oncological radicality. Methods: Data were collected from a multicenter retrospective database that includes 1070 consecutive robotic liver resections (RLRs) performed in nine European hospital centers from 2011 to 2023. Of the entire series, 343 liver resections were performed for HCC. Results: A total of 247 patients (72.3%) had mono-focal lesions. Major hepatectomies and anatomical resections have been perfomed in 87% and 55% of patients, respectively. All 17 conversions (4.95%) were to the open approach. The operative mean time was 239.56 min and the estimated blood loss was 229.45 mL. The overall post-operative complication rate was 22.74%, but severe complications occurred in 4.08% of patients and one of them (0.29%) was reoperated on. The mean hospital stay was 5.82 days with a mean ICU stay of 0.9 days. Twenty-six resections (7.6%) were R1 parenchymal. Forty-six patients (4.08%) were readmitted to the hospital within 90 days after discharge and seventy-eight patients (22.74%) had disease recurrence. Total deaths included 36 (10.5%) patients with a 90-day mortality of 0.9%. Conclusions: Robotic liver resection for HCC is feasible and safe when performed in experienced centers by surgeons who have completed the learning curve. Full article

The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion intention is estimated online by the neural network (NN) to cope with the intervention of the patient’s subjective motor awareness in the late stage of rehabilitation training. Due to the differences in impedance parameters for training tasks in individual patients and periods, the least square method was used to learn the impedance parameters of the patient. Considering the uncertainties of the exoskeleton and the safety of rehabilitation training, an adaptive neural network impedance controller with output constraints was designed. The NN was applied to approximate the unknown dynamics and the barrier Lyapunov function was applied to prevent the system from violating the output rules. The feasibility and effectiveness of the proposed strategy were verified by simulation. Full article

Due to the uncertainty and complexity of the assembly process, the trajectory planning of a robot needs to consider the real-time obstacle avoidance problem when it completes the assembly in the unstructured workspace. To realize the safe assembly of assembly robots in dynamic and complex environments, a dynamic obstacle avoidance trajectory planning method for robots combining traditional planning algorithms and deep reinforcement learning algorithms is proposed to improve the robot’s agent and obstacle avoidance ability in dynamic and complex environments. The Bidirectional Rapidly-exploring Random Tree (Bi-RRT) method is utilized as a global planner to plan the global optimal path quickly; considering the real-time nature of the assembly process, the Soft Actor-Critic (SAC) is used as a local obstacle avoider to avoid obstacles more accurately and to find the nearest node generated by the Bi-RRT during the planning process, which is regarded as the goal during the local obstacle avoidance to reduce the model’s complexity. By training and testing in the simulation engine and comparing with SAC, DDPG and DQN algorithms, the method can avoid obstacles in dynamic and complex environments more efficiently, which verifies that the proposed hybrid method can accomplish the high-precision planning task with a high success rate. Full article

The emotional response of robotics is crucial for promoting the socially intelligent level of human–robot interaction (HRI). The development of machine learning has extensively stimulated research on emotional recognition for robots. Our research focuses on emotional gaits, a type of simple modality that stores a series of joint coordinates and is easy for humanoid robots to execute. However, a limited amount of research investigates emotional HRI systems based on gaits, indicating an existing gap in human emotion gait recognition and robotic emotional gait response. To address this challenge, we propose a Gait-to-Gait Emotional HRI system, emphasizing the development of an innovative emotion classification model. In our system, the humanoid robot NAO can recognize emotions from human gaits through our Trajectories-Aware and Skeleton-Graph-Aware Spatial–Temporal Transformer (TS-ST) and respond with pre-set emotional gaits that reflect the same emotion as the human presented. Our TS-ST outperforms the current state-of-the-art human-gait emotion recognition model applied to robots on the Emotion-Gait dataset. Full article

The dairy supply chain encompasses all stages involved in the production, processing, distribution, and delivery of dairy products from farms to end consumers. Artificial intelligence (AI) refers to the use of advanced technologies to optimize processes and make informed decisions. Using the PRISMA methodology, this research analyzes AI technologies applied in the dairy supply chain, their impact on process optimization, the factors facilitating or hindering their adoption, and their potential to enhance sustainability and operational efficiency. The findings show that artificial intelligence (AI) is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain. These tools enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability. The use of machine learning algorithms, such as Tabu Search, ACO, and SARIMA, is highlighted for predicting production, managing inventories, and optimizing logistics. Additionally, AI fosters sustainability by reducing environmental impact through more responsible farming practices and process automation, such as robotic milking. However, its adoption faces barriers such as high costs, lack of infrastructure, and technical training, particularly in small businesses. Despite these challenges, AI drives operational efficiency, strengthens food safety, and supports the transition toward a more sustainable and resilient supply chain. It is important to note that the study has limitations in analyzing long-term impacts, stakeholder resistance, and the lack of comparative studies on the effectiveness of different AI approaches. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article

The Mecanum-wheeled robot has four special wheels. It can control four wheels independently and has seven turning axes. The robot can translate in all directions and travel in curves without changing its direction by means of the control commands for turning ratio, speed, and direction of travel. However, no model has been proposed that can accurately simulate the output of the actual machine for the three types of inputs, even when the characteristics of the motor and motor driver are unknown. In this study, we synthesized and simplified transfer functions and estimated the undetermined coefficients that minimize the sum of squared errors to construct a model of the robot that can output the position and posture equivalent to those of the actual robot for the input commands for turning ratio, speed, and the direction of travel. We modeled a Mecanum-wheeled robot using the proposed modeling method and parameter determination method and compared the outputs of the real robot to the step and ramp inputs. The results showed that the errors between the two outputs were very small and accurate enough to simulate AI learning, such as reinforcement learning, using the model of the robot. Full article

Soft arms, characterized by their compliance and adaptability, have gained significant attention in applications ranging from industrial automation to biomedical fields. Modeling these systems presents unique challenges due to their high degrees of freedom, nonlinear behavior, and complex material properties. This review provides a comprehensive overview of three primary modeling approaches: numerical methods, analytical techniques, and data-driven models. Numerical methods, including finite element analysis and multi-body dynamics, offer precise but computationally expensive solutions for simulating soft arm behaviors. Analytical models, rooted in continuum mechanics and simplified assumptions, provide insights into the fundamental principles while balancing computational efficiency. Data-driven approaches, leveraging machine learning and artificial intelligence, open new avenues for adaptive and real-time modeling by bypassing explicit physical formulations. The strengths, limitations, and application scenarios of each approach are systematically analyzed, and future directions for integrating these methodologies are discussed. This review aims to guide researchers in selecting and developing effective modeling strategies for advancing the field of soft robotic arm design and control. Full article

Construction robots are increasingly becoming a significant force in the digital transformation and intelligent upgrading of the construction industry. Path planning is crucial for the advancement of building robot technology. Based on the understanding of construction site information, this paper categorizes path-planning algorithms into two types: global path-planning and local path-planning. Local path planning is further divided into classical algorithms, intelligent algorithms, and reinforcement learning algorithms. Using this classification framework, this paper summarizes the latest research developments in path-planning algorithms, analyzes the advantages and disadvantages of various algorithms, introduces several optimization strategies, and presents the results of these optimizations. Furthermore, common environmental modeling methods, path quality evaluation criteria, commonly used sensors for robots, and the future development of path-planning technologies in swarm-based construction robots are also discussed. Finally, this paper explores future development trends in the field. The aim is to provide references for related research, enhance the path-planning capabilities of construction robots, and promote the intelligent development of the construction industry. Full article

This paper demonstrates the performance of autonomous rovers utilizing NeuroEvolution of Augmenting Topologies (NEAT) in multi-room scenarios and explores their potential applications in wildfire management and search and rescue missions. Simulations in three- and four-room scenarios were conducted over 100 to 10,000 generations, comparing standard learning with transfer learning from a pre-trained single-room model. The task required rovers to visit all rooms before returning to the starting point. Performance metrics included fitness score, successful room visits, and return rates. The results revealed significant improvements in rover performance across generations for both scenarios, with transfer learning providing substantial advantages, particularly in early generations. Transfer learning achieved 32 successful returns after 10,000 generations for the three-room scenario compared to 34 with standard learning. In the four-room scenario, transfer learning achieved 32 successful returns. Heatmap analyses highlighted efficient navigation strategies, particularly around starting points and target zones. This study highlights NEAT’s adaptability to complex navigation problems, showcasing the utility of transfer learning. Additionally, it proposes the integration of NEAT with UAV systems and collaborative robotic frameworks for fire suppression, fuel characterization, and dynamic fire boundary detection, further strengthening its role in real-world emergency management. Full article

The TacTip is a cost-effective, 3D-printed optical tactile sensor commonly used in deep learning and reinforcement learning for robotic manipulation. However, its specialized structure, which combines soft materials of varying hardnesses, makes it challenging to simulate the distribution of numerous printed markers on pins. This paper aims to create an interpretable, AI-applicable simulation of the deformation of TacTip under varying pressures and interactions with different objects, addressing the black-box nature of learning and simulation in haptic manipulation. The research focuses on simulating the TacTip sensor’s shape using a fully tunable, chain-based mathematical model, refined through comparisons with real-world measurements. We integrated the WRS system with our theoretical model to evaluate its effectiveness in object pose estimation. The results demonstrated that the prediction accuracy for all markers across a variety of contact scenarios exceeded 92%. Full article

The application of modern machine learning methods in industrial settings is a relatively new challenge and remains in the early stages of development. Current computational power enables the processing of vast numbers of production parameters in real time. This article presents a practical analysis of the welding process in a robotic cell using the unsupervised HDBSCAN machine learning algorithm, highlighting its advantages over the classical k-means algorithm. This paper also addresses the problem of predicting and monitoring undesirable situations and proposes the use of the real-time graphical representation of noisy data as a particularly effective solution for managing such issues. Full article