Search Results (332)

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural networks), this adaptive capability enables the ZNN to rapidly and accurately solve time-varying problems. By leveraging dynamic zeroing error functions, the ZNN exhibits distinct advantages in addressing complex time-varying challenges, including matrix inversion, nonlinear equation solving, and quadratic optimization. This paper provides a comprehensive review of the evolution of ZNN model formulations, with a particular focus on single-integral and double-integral structures. Additionally, we systematically examine existing nonlinear activation functions, which play a crucial role in determining the convergence speed and noise robustness of ZNN models. Finally, we explore the diverse applications of ZNN models across various domains, including robot path planning, motion control, multi-agent coordination, and chaotic system regulation. Full article

This paper proposes an enhanced RRT* algorithm (XN-RRT*) to address the challenges of low path planning efficiency and suboptimal picking success rates in complex pitaya harvesting environments. The algorithm generates sampling points based on normal distribution and dynamically adjusts the center and range of the sampling distribution according to the target distance and tree density, thus reducing redundant sampling. An improved artificial potential field method is employed during tree expansion, incorporating adjustment factors and target points to refine the guidance of sampling points and overcome local optima and infeasible targets. A greedy algorithm is then used to remove redundant nodes, shorten the path, and apply cubic B-spline curves to smooth the path, improving the stability and continuity of the robotic arm. Simulations in both two-dimensional and three-dimensional environments demonstrate that the XN-RRT* algorithm performs effectively, with fewer iterations, high convergence efficiency, and superior path quality. The simulation of a six-degree-of-freedom robotic arm in a pitaya orchard environment using the ROS2 platform shows that the XN-RRT* algorithm achieves a 98% picking path planning success rate, outperforming the RRT* algorithm by 90.32%, with a 27.12% reduction in path length and a 14% increase in planning success rate. The experimental results confirm that the proposed algorithm exhibits excellent overall performance in complex harvesting environments, offering a valuable reference for robotic arm path planning. Full article

As the population continues to grow, the need for increased food production has become increasingly evident. However, the agricultural workforce is decreasing due to industrialization. To address this gap, numerous research efforts are underway to adopt robots for harvesting. While significant progress has been made, especially in fruit harvesting using robotic systems, there are still inefficiencies and areas where human intervention is necessary. This paper proposes an algorithm for selecting the position of a mobile manipulator for fruit harvesting using an inverse reachability map to achieve automation and enhance efficiency. Additionally, a method for collision-free path planning in joint space using the model predictive artificial potential field (MPAPF) algorithm is proposed. The effectiveness of the proposed position selection and path planning algorithms was validated through harvesting simulations and a collision-free path was validated in experiments. Full article

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during vehicle movement. The goal is to improve the robot’s navigation autonomously and ensure the safety of people and equipment in dynamic environments. Using this technique, it is possible to provide important references to the robot’s internal control system, guiding it to continuously adjust its velocity and yaw in order to avoid obstacles (humans and moving objects) while following the path planned for its task. The approach aims to improve operational safety without compromising productivity, addressing critical challenges in collaborative robotics. The system was tested in a simulated environment using the Robot Operating System (ROS) and Gazebo to demonstrate the effectiveness of navigation and obstacle avoidance. The results obtained with the application of the proposed technique indicate that the framework allows real-time adaptation and safe interaction between robot and obstacles in complex and changing industrial workspaces. Full article

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation. Full article

Bhutan is a developmental success story that since the 1960s has consistently used five-year national development plans to make substantial socio-economic progress and promote sustainability. Many other developing countries had abandoned medium-term national planning by the 1980s, but Bhutan continued using these instruments as the principal mechanisms for developing the country and making substantial welfare gains for its population while attending to the sustainability of its development path and environment. Poverty has been greatly reduced, incomes have grown in real terms, life expectancy has markedly increased, there has been enormous growth in the provision of education and the country has become a world leader in environmental protection with 71% of Bhutan still under forest, making it the first carbon negative country in the world. The reasons for Bhutan’s success include always working within the capabilities of government, economy, and society; a demonstrated capacity to reorient development strategies with new plans; a stable political environment; good governance; the gradual inclusion of multiple stakeholders into the development process; a strong supportive relationship with neighbouring India; and the wise use of income from hydropower to fund development. Though there have been disruptions, mistakes, and failures in planning in Bhutan, the general trajectory of solid progress and continued attention to sustainability has been maintained; however, there are considerable challenges ahead for the ambitious Thirteenth Five-Year Plan (2024–2029). To investigate the Bhutanese experience, this article adopts a qualitative case study approach. Full article

Wind energy’s crucial role in global sustainability necessitates efficient wind turbine maintenance, traditionally hindered by labor-intensive, risky manual inspections. UAV-based inspections offer improvements yet often lack adaptability to dynamic conditions like blade pitch and wind. To overcome these limitations and enhance inspection efficacy, we introduce the Dynamic Trajectory Adaptation Method (DyTAM), a novel approach for automated wind turbine inspections using UAVs. Within the proposed DyTAM, real-time image segmentation identifies key turbine components—blades, tower, and nacelle—from the initial viewpoint. Subsequently, the system dynamically computes blade pitch angles, classifying them into acute, vertical, and horizontal tilts. Based on this classification, DyTAM employs specialized, parameterized trajectory models—spiral, helical, and offset-line paths—tailored for each component and blade orientation. DyTAM allows for cutting total inspection time by 78% over manual approaches, decreasing path length by 17%, and boosting blade coverage by 6%. Field trials at a commercial site under challenging wind conditions show that deviations from planned trajectories are lowered by 68%. By integrating advanced path models (spiral, helical, and offset-line) with robust optical sensing, the DyTAM-based system streamlines the inspection process and ensures high-quality data capture. The dynamic adaptation is achieved through a closed-loop control system where real-time visual data from the UAV’s camera is continuously processed to update the flight trajectory on the fly, ensuring optimal inspection angles and distances are maintained regardless of blade position or external disturbances. The proposed method is scalable and can be extended to multi-UAV scenarios, laying a foundation for future efforts in real-time, large-scale wind infrastructure monitoring. Full article

Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%. Full article

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

Visually impaired people face significant obstacles when navigating complex environments. However, recent technological advances have greatly improved the functionality of navigation systems tailored to their needs. The objective of this research is to evaluate the effectiveness and functionality these navigation systems through a comparative analysis of recent technologies. For this purpose, the PRISMA 2020 methodology was used to perform a systematic literature review. After identification and screening, 58 articles published between 2019 and 2024 were selected from three academic databases: Dimensions (26 articles), Web of Science (18 articles), and Scopus (14 articles). Bibliometric analysis demonstrated a growing interest of the research community in the topic, with an average of 4.552 citations per published article. Even with the technological advances that have occurred in recent times, there is still a significant gap in the support systems for people with blindness due to the lack of digital accessibility and the scarcity of adapted support systems. This situation limits the autonomy and inclusion of people with blindness, so the need to continue developing technological and social solutions to ensure equal opportunities and full participation in society is evident. This study emphasizes the great advances with the integration of sensors such as high-precision GPS, ultrasonic sensors, Bluetooth, and various assistance apps for object recognition, obstacle detection, and trajectory generation, as well as haptic systems, which provide tactile information through wearables or actuators and improve spatial awareness. Current navigation algorithms were also identified in the review with methods including obstacle detection, path planning, and trajectory prediction, applied to technologies such as ultrasonic sensors, RGB-D cameras, and LiDAR for indoor navigation, as well as stereo cameras and GPS for outdoor navigation. It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. Finally, analysis revealed that 79% of the 58 reviewed articles included experimental validation, 87% of which were on haptic systems and 40% on smartphones. These results underscore the importance of experimentation in the development of technologies for the mobility of people with visual impairment. Full article

The removal of rust from large equipment such as trains and ship hulls poses a significant challenge. Traditional methods, such as chemical cleaning, flame rust removal, and laser rust removal, suffer from drawbacks such as high energy consumption, operational complexity, and poor mobility. Sandblasting and high-pressure water jet rust removal face issues such as high consumable costs and environmental pollution. Existing robotic grinding systems often rely on precise measurement of the workpiece surface geometry to perform deburring and polishing tasks; however, they lack the sufficient adaptability and robustness required for rust removal operations. To address these limitations, this study proposes a floating grinding actuator scheme based on compound force-position fuzzy control. By implementing simplified path-point planning, continuous grinding and rust removal can be achieved without requiring the pre-measurement of workpiece geometry data. This solution integrates force and laser displacement sensors to provide real-time compensation for path deviations and ensures adaptability to complex surfaces. A fuzzy derivative-leading PID algorithm was employed to control the grinding force, enabling adaptive force regulation and enhancing the control precision. Rust removal test results demonstrate that under varying advancing speeds, fuzzy derivative-leading PID control can significantly reduce fluctuations in both the grinding force and average error compared to traditional PID control. At a speed of 40 mm/s, excellent control performance was maintained, achieving a rust removal rate of 99.73%. This solution provides an efficient, environmentally friendly, and high-precision automated approach to rust removal using large-scale equipment. Full article

Multi-island cavities are common and complex features in structural parts of the aerospace, energy, and power fields. The processing is hindered by low programming efficiency and a strong dependence on the experience of process engineers. In response to these challenges, this paper proposes a highly efficient and robust contour-parallel tool path planning method aimed at improving the rough machining efficiency and quality of multi-island cavities. The method decomposes the complex cavity into multiple sub-regions based on angular geometric features. Subsequently, a closed boundary is formed by connecting the islands with the outer contour using the bridge algorithm. On this base, the method applies rule-based criteria to assess the validity of offset intersections and extracts valid closed loops through point tracing, effectively mitigating both local and global interferences. This approach guarantees the generation of smooth and stable contour-parallel tool paths. The tool path experiments on multiple multi-island cavities demonstrate that the proposed method is capable of automatically generating continuous, interference-free, and residue-free machining paths, thus significantly enhancing machining efficiency and surface quality. Full article

This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm’s capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces. Full article

Over the past two decades, walkability, accessibility, and urban street culture have become major study topics in several areas of contemporary urban research, including urban sustainability, urban economy, healthy cities, and the x-minute city. Due to a plethora of evidence that supports the benefits of an accessible and walkable neighborhood, many countries and cities have put in place urban reform agendas that prioritize accessibility and walkability and promote urban street culture. Saudi Arabia is among those countries, as evidenced by the goals established in Saudi Vision 2030. This study focuses on the City of Hail’s efforts to enhance the walkability of its neighborhoods and the city’s accessibility. This study looks at how the newly constructed pedestrian infrastructure matches people’s expectations and how it influences how people in Hail walk. This study also makes specific suggestions for improvement and identifies ways forward. This study employs a three-fold ‘post-occupancy evaluation’ methodology that includes qualitative interviews, quantitative surveys, and direct observation, focusing on how the community interacts with the new pedestrian streetscapes. This study recommends designing areas in the City of Hail with improved pedestrian rights-of-way, enhancing sidewalk design and continuity, creating pedestrian buffer zones, boosting shade and shelter, and increasing safety and security. The suggested design changes will have the added benefit of strengthening the sense of community of Hail residents while also promoting mixed-use development, which is generally recognized as a more ‘organic’, natural development path that also aligns with Saudi’s heritage architecture, returning Hail’s urban space to its roots. These findings are crucial for shaping city planning in the City of Hail and beyond by emphasizing inclusive strategies that create lively communities where walking is encouraged and enjoyed. Full article