Optimized robotic setup for automated active thermography using advanced path planning and visibility study.

Assembling a product is a delicate process at the borderline between design and manufacturing. Shortening the time between them plays a central role both for quality assurance and fast time to market. In this paper, we describe an automatic tool based on a new method, integrating assembly design, sequence optimization, and advanced rigid body path planning. First, we introduce a greedy algorithm for assembling a product, part by part, based on state-of-the-art path planning. We exploit all the six degrees of freedom of a rigid body to search for collision-free paths, instead of limited motions. Then, we use assembly design in order to limit the search for an optimal assembling sequence and to guarantee geometrical quality among the sequences examined. Disassembly path planning is used here to further cut the state space and to give a quality measure to the sequences. Eventually, we present results for an industrial test case, which has been successfully solved by applying our method.

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

This paper aims at providing a solution to Optimum Power Flow (OPF) in practical power systems by using a flexible genetic algorithm (GA) model. The proposed approach finds the optimal setting of OPF control variables which include generator active power output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimising the fuel cost. The proposed GA is modelled to be flexible for implementation to any practical power systems with the given system line, bus data, generator fuel cost parameter and forecasted load demand. The GA model has been analysed and tested on the standard IEEE 30-bus system and two real practical power systems which are an industrial park power system and a gold-copper mining power system both located in Indonesia. These case studies of real power systems have been performed using actual data and the demand pattern. The results obtained outperform other approaches from the literature which was recently applied to the IEEE 30-bus system with the same control variable limits and system data. Better results are also found when compared against the configurations used in the two real power systems which are heuristic based on the practical expertise of power plant engineers. These superior results are achieved due to the robust and reliable algorithm of the proposed GA which utilises the elitism and non-uniform mutation rate.

Genetic Programming (GP) homologous crossovers are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. In this paper we present an exact schema theory for GP and variable-length Genetic Algorithms (GAs) which is applicable to this class of operators. The theory is based on the concepts of GP crossover masks and GP recombination distributions that are generalisations of the corresponding notions used in GA theory and in population genetics, as well as the notions of hyperschema and node reference systems, which are specifically required when dealing with variable size representations. In this paper we also present a Markov chain model for GP and variable-length GAs with homologous crossover. We obtain this result by using the core of Vose's model for GAs in conjunction with the GP schema theory just described. The model is then specialised for the case of GP operating on 0/1 trees: a tree-like generalisation of the concept of binary string. For these, symmetries exist that can be exploited to obtain further simplifications. In the absence of mutation, the Markov chain model presented here generalises Vose's GA model to GP and variable-length GAs. Likewise, our schema theory generalises and refines a variety of previous results in GP and GA theory.

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N‐P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

In this paper, we use a compact 6 degree-of- freedom (DOF) robot arm as the actuator of the venipuncture system. Aiming at the robot arm’s requirements for speed and smoothness and considering that the robot arm may collide with obstacles in the course of movement,we simplify the model of the robot arm and obstacles according to the geometry of the robot arm and obstacles in the environment. Then we proposed an improved A-star algorithm and a three-dimensional (3D) collision detection algorithm, which can plan a collision-free optimal path for the blood taking needle fixed in the end of the robot arm, so that the blood taking needle can penetrate into the target point of the venous vessel continuously,smoothly and accurately driven by the robot arm. The simulation and actual path planning experiments indicate that the proposed path planning method is effective and feasible.

This paper is oriented to the transmission line inspection environment, aiming to solve the problem of how to control the robotic arm to quickly and accurately grasp the target object, and work on the planning of the motion path and the position-based visual servo control method. For the spatial trajectory planning of the robotic arm, linear trajectory planning and circular arc trajectory planning are carried out respectively. Between the reaction speed problem of the traditional RRT motion algorithm in collision-free motion planning, a two-way RRT-Connect path planning method is proposed. Then the visual servo control algorithm of the UAV robotic arm is proposed. The position-based visual servo control method is adopted, while the EK-SVSF position estimation algorithm and the OWA-based stepwise data fusion algorithm are combined for optimal position estimation of the target during transmission line inspection. It is verified in simulation experiments that the improved RRT algorithm can obtain the shortest path planning in a shorter time with better performance. Finally, the grasping experiment is carried out in a real robotic arm transmission line inspection scene. The robotic arm path planning algorithm and visual servo control proposed in this paper are combined to carry out grasping experiments in a static cluttered environment. The experimental results show that the algorithm proposed in this paper has the best grasping success rate, servo success rate, and grasping similarity. In addition, the average errors in x-axis, y-axis and z-axis of the proposed method are 0.19cm, 1.11cm and 0.18cm, respectively, which show a good grasping accuracy, indicating that the control method proposed in this paper can be applied to the real transmission line inspection work scenarios.

The robot's arm used to be limited to manipulation by the industrial manipulator, and most studies on the humanoid robot focus on robots' walking methodologies. Since the multi-DOF (degree of freedom) system, like the humanoid robot's arm, has a high- dimension configuration space that cannot appear geometrically and that has many kinematics and dynamics constraints, the path planning of the system requires much computation and, as such, is very complicated. In this paper, the path planning algorithm was employed for the humanoid robot's (Maru-1) arms. The RRT (rapidly exploring random tree) algorithm, which has been proven to be an efficient approach to multi-arm motion plans, was selected for use in this study. The employed algorithm, with inverse-kinematics analysis, was evaluated in this study using 3D graphic simulation.

Job shop scheduling is one of the complex problems in the manufacturing industry, such as an automotive body manufacturer. This manufacturing company, located in Cikarang, Indonesia, deals with huge customer demand that leads to difficulties in production scheduling. This will cause a delay in some jobs and product deliveries. The current system is using the semi-active scheduling approach and requires 637 minutes for performing 6 jobs with 5 machines. The genetic algorithm (GA) model is proposed as an alternative solution to solve this problem. The GA parameter is set as follow: The population size expected is 30 with maximum generation can be produced in amount of 50. The crossover rate, mutation, and preservation are set to 0.3, 0.1, and 0.1, respectively. After 50 generations are obtained, the optimum solution is shown in generation 6 with a makespan of 597 minutes. Thus, the genetic algorithm model is effectively reducing the makespan of the job-shop scheduling problem by 10% compared to the current method applied at the company.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO2 emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO2 emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO2 emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO2 emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO2 emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO2 emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO2 emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP.

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the predefined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.

In mobile robot research domain and especially in its path planning sub-domain, the optimization tools the most used are PSO (Particle Swarm Optimization) and GA (Genetic Algorithms). In fact, mobile robot path planning is a multi-objective optimization problem where at least these objectives are considered: the shortest and the safest path to the goal. Sometimes other objectives are added like travel duration. The subject of this paper is a comparison investigation concerning the efficiency of PSO and GA in mobile robot path planning optimization. We start with a comparison of evolutionary performances of PSO and GA. Then, we apply PSO and GA in the optimization of parameters of DVSF\(^{2}\) (Dynamic Variable Speed Force Field) path planning approach. Different environments of different types, statics and dynamics are considered in these simulations to decide which is better for mobile robot path planning optimization: PSO or GA.

Rapid advances in the world of high-speed optical network and high-speed electronic routing has led to consideration of advanced models such as genetic algorithms (GAs). GAs have proved to be a practical and robust optimization and search tool for network design. However, a GA is not generally employed in traffic grooming over mesh networks. In this paper, we propose a new GA model to handle the traffic grooming problem in an all-optical WDM mesh network. The new model extends classical GAs with heuristic approach to support network cost optimization for combining multiple traffic streams into a single lightpath. In addition, to support our new GA model, we have developed a set of new investigation including position-based bit matrix encoding, genetic heuristic operators, and fitness evaluation function using the clustering method. The experimental results show that our GA model is superior to traditional heuristic approaches for the 5, 6 and 14-node benchmark networks.

Identifying feasible pathways for safe endovascular tool navigation is necessary for preoperative planning, intraoperative guidance, and robotic-assisted procedures. However, current path-planning approaches do not guarantee that optimal paths can be found due to limitations in path exploration. As such, this paper proposes a novel approach using Bi-directional Rapidly Exploring Random Trees (BiRRT) with a Genetic Algorithm (GA) to generate optimal paths in vascular environments. This approach initializes a population of Bi-RRT paths and performs GA to arrive at the optimal path in terms of path length, smoothness, and safety. Path planning simulations were performed across three 2D representations of aortic arches to model path planning in endovascular surgery. Results indicate that the algorithm produced optimal paths compared to a standalone Bi-RRT and a centerline-based path planning algorithm for endovascular surgery. The resulting paths also demonstrate that the proposed method produced shorter, smoother, and safer paths across different environments than other algorithms. These findings demonstrate that combining Bi-RRT with GA is a promising approach for improving path planning in endovascular surgery, potentially leading to safer and more efficient procedures.