Fully integrated artificial intelligence solution for real time route tracking

A new method, Global Newton-Raphson (GNR), for forward kinematics analysis of six degree-of-freedom (DOF) parallel robots is proposed, in order to obtain direct solutions of parallel robots in real time without divergence. The parallel robot can produce 3-DOF linear motions and 3-DOF angular motions, and feeds back positions of actuators measured by position sensors in real time. The GNR for the robot is built by Taylor series expansion and Newton-Raphson iteration with descent operator. Applying the GNR, the real generalized pose of moving platform is solved with respect to the base for a set of given positions of actuators. Accuracy and real time property of the GNR for the 6-DOF parallel robot are analyzed. The theoretical analysis indicated that the presented method can achieve the numerical convergent solution in real time with high accuracy, even the initial guesses are far from a solution.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

In this paper, Artificial Intelligence (AI) is developed for studying the Boussinesq Paradigm equation and so called b-equation based on Physics-Informed Cellular Neural Networks (PICNNs). The models studied here come from fluid dynamics. Machine learning through Physics-Informed Neural Networks (PINNs) is a powerful tool for solving complex problems arising in physical laws. By optimization and automatic differentiation, the solutions of the model under consideration can be approximated precisely and can be obtained in real time. In this paper, we shall apply a new algorithm based on Physics-Informed Cellular Neural Networks (PICNNs) for obtaining the interactions between solitons and peakons. The algorithm has many advantages, but the main ones are that it provides the fastest programming and solutions in real time. It is known that Cellular Neural Networks (CNNs) have the ability to approximate, in a very accurate way, nonlinear partial differential equations (PDEs) and to present their solutions in real time. By incorporating the physical laws into the learning process through PICNN we can solve various problems from fluid dynamics, material science, and quantum mechanics.

The industrial robots play an important role in modern industrial manufacturing processes, and their applications are becoming increasingly widespread, greatly improving manufacturing efficiency and significantly enhancing the digitalization level of the manufacturing industry. Therefore, achieving safe control of multi robot collaborative work is of great significance for the stable and efficient operation of robot systems. This article proposes a dual robot safety control technology based on the kinematic model of industrial robots. Firstly, a mathematical model of the kinematic space of the dual robots is constructed using D-H parameters; Then, use recursive traversal algorithm to calculate the minimum distance between each joint arm of the two robots in real time; Finally, the minimum distance between each joint arm will be calculated and compared with the set safety threshold to achieve real-time assessment and control of collision risk during the movement of the dual robots. A dual robot safety control testing platform was built using two KUKA industrial robots, integrated control software was developed, and relevant algorithms were validated. Through experiments, it was verified that this method can evaluate the collision risk of dual robots in real time during motion and effectively control the collision risk during the motion of dual robots.

A high‐speed field‐programmable gate array (FPGA) design is proposed to calculate angles and distances for biped robots in real time. A low‐complexity and high‐accuracy hardware‐oriented algorithm based on CORDIC was developed. To reduce hardware cost, a hardware sharing technique was used to realise a CORDIC scaling factor generator and three hardware sharing machines. Moreover, the multipliers and dividers were replaced by cost‐efficiency components, such as adders and shifters to further reduce hardware cost. In addition, the proposed design was implemented with a fully pipelined architecture, which achieved improved operating frequency and throughput efficiency. The proposed design was realised and verified by an FPGA device with a maximum operating frequency of 127 MHz, which achieved the calculation of angles and distances for biped robots in real time. Compared with the previous design, this work not only reduced hardware cost by at least 49.6% and average errors by at least 67.2%, but also improved the average executing performance by 62.7% when calculating ten angles for the biped robots.

Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.

As robotics technologies evolve rapidly, path planning has emerged as a fundamental pillar underpinning autonomous navigation systems. Traditional algorithms such as A*, Dijkstra, and Bellman-Ford demonstrate competence in structured environments, yet exhibit notable limitations in dynamic and uncertain scenarios. In contrast, intelligent algorithmsincluding Genetic Algorithms (GA), Reinforcement Learning (RL), and hybrid strategiesprovide measurable advantages in adaptability, learning capacity, and robustness. This study centers on comparing traditional and intelligent control algorithms for path planning from a theoretical perspective, outlining their respective principles, performance traits, and application contexts. The findings suggest that intelligent algorithms, owing to their capacity for real-time learning, environmental adaptation, and heuristic generalization, consistently outperform traditional approaches in dynamic and unstructured operational contexts. The paper offers critical theoretical insights to guide future algorithm selection and optimization for robot navigation. Furthermore, the paper discusses real-world implementation challenges, such as computational complexity, sensor uncertainty, and algorithm convergence stability, to highlight the applicability of these techniques in complex systems.

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Intelligent optimization algorithm (IOA) has been widely studied and applied to solve various optimization problems. When scholars improve IOA with mathematical methods, they also want to seek an effective method to implement algorithms with higher real time, especially for a complex problem. Parallel design is an effective method to improve the real time of IOA. Currently, the parallel programming based on open multi-processing (OpenMP) and compute unified device architecture (CUDA) are two popular methods. To find and develop a new IOA parallel method, in this paper, a parallel design and implementation method based on field programmable gate array (FPGA) is explored. In order to validate the proposed method, parallel genetic algorithm (GA) and parallel particle swarm optimization (PSO) algorithm are realized by the proposed method. Furthermore, the performance and advantage of the proposed FPGA-based parallel IOA method are tested by comparing with OpenMP-based parallel programming and CUDA-based parallel programming, the final results show that the proposed method with highest real-time performance in IOA parallel implementation. A case study by using FPGA-based parallel simulate annealing (SA) to address job shop scheduling problem (JSSP) to illustrate the proposed method has high potential in industrial applications.

Decontamination and Dismantling (D&D) activities within the U.S. Department of Energy (DOE) require a long reach manipulator with a large load capacity. Variable Geometry Trusses (VGTs) are a unique class of mechanical structures which allow the advantages of truss structures for large scale applications to be applied to large robotic manipulators. Individual VGT units may be assembled to create a modular, long-reach, truss-type manipulator. Each module of such a manipulator system is either a static truss section or one of several possible VGT geometries. While many potential applications exist for this technology, the present work is largely motivated by the need for generic robotic systems for remote manipulation. A manipulator system based on VGT modules provides several advantages. The reconfigurable nature of the manipulator system allows it to be adapted on site to unforeseen conditions. The kinematic redundancy of the manipulator enables it to work effectively even in a highly obstructed workspace. The parallel structure of the truss modules enables the manipulator to be withdrawn in the event of a structural failure. Finally, the open framework of the modules provides a clear, protected passageway for control and power cabling, waste conveyance, or other services required at the end effector. As is implied in a truss structure, all primary members of a VGT are ideally loaded in pure tension or compression. This results in an extremely stiff and strong manipulator system with minimal overall weight. Careful design of the joints of a VGT is very important to the overall stiffness and accuracy of the structure, as several links (as many as six) are joined together at each joint. The greatest disadvantage to this approach to manipulator design has traditionally been that the kinematics of VGT structures are complex and poorly understood. This report specifically addresses the kinematics of several possible geometries for the individual VGT units. Equations and solution techniques are developed for solving the "forward" or "direct" and "inverse" kinematic problems for these geometries. The" forward" kinematic problem is that of finding the position and orientation of the distal end of the VGT relative to the proximal end, given the specific displacements of the (linear) actuators. This problem is rarely solvable in closed form. However, powerful iterative algorithms capable of solution in real time on typical modern robot control hardware are presented. The "inverse" kinematic problem is that of finding the required actuator displacements given the position and orientation of the distal end of the VGT relative to the proximal end. For specific VGT geometries, closed-form solutions are presented. For the more general problem, iterative algorithms capable of solution in real time are again derived and presented.

Microgrids with renewable distributed generation and energy storage offer sustainable energy solutions. To maintain the availability of energy to the connected loads, considering priority and to interrupt the smallest portion of the microgrid under any abnormal conditions, reconfiguration is critical to restore service to a section or to meet some operational requirements of dropping minimum loads. Reconfiguration is the process of modifying the microgrid's topological structure by changing the status (open/close) of the circuit breakers or switches. In this work, constraints are the power balance equation and power generation limits, and we assumed that the system is designed with the entire planning and operational control criterion to meet the voltage violation and line overloading constraints. This paper offers novel real-time implementation of intelligent algorithm for microgrid reconfiguration. Intelligent algorithm is based on the genetic algorithms and has been tested on two test systems including shipboard power system and modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Real-time test bed utilizes real-time digital simulator and commercial real-time controllers from Schweitzer Engineering Lab. Reconfiguration algorithm has been implemented in the real time using real-time test bed, e.g., microgrid system, and satisfactory results were obtained.

In view of the present shaft alignment to optimize focused on one-way location optimization, and less the method for shaft alignment optimization. By MATLAB invoking ANSYS, the finite element method is combined with intelligent algorithm, which is used to optimization calculation for shaft test platform. In the calculation, the minimum stern shaft bearing load as the goal, the vertical shaft bearing and axial location is optimized based on a new intelligent optimization algorithm-invasive weed algorithm. Compared with the linear programming and genetic algorithm, the results showed that the invasive weeds algorithm has better global optimization ability. ANSYS simulation combined with MATLAB can provide a new approach for shafting design, shafting alignment and vibration calculation.

Recently, the first oligopolistic competition model of the closed-loop supply chain network involving uncertain demand and return has been established. This model belongs to the context of oligopolistic firms that compete noncooperatively in a Cournot-Nash framework. In this paper, we modify the above model in two different directions. (i) For each returned product from demand market to firm in the reverse logistics, we calculate the percentage of its optimal product flows in each individual path connecting the demand market to the firm. This modification provides the optimal product flow routings for each product in the supply chain and increases the optimal profit of each firm at the Cournot-Nash equilibrium. (ii) Our model extends the method of finding the Cournot-Nash equilibrium involving smooth objective functions to problems involving nondifferentiable objective functions. This modification caters for more real-life applications as a lot of supply chain problems involve nonsmooth functions. Existence of the Cournot-Nash equilibrium is established without the assumption of differentiability of the given functions. Intelligent algorithms, such as the particle swarm optimization algorithm and the genetic algorithm, are applied to find the Cournot-Nash equilibrium for such nonsmooth problems. Numerical examples are solved to illustrate the efficiency of these algorithms.