A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm

Tractor-trailer wheeled mobile robot is a complicated system, which is composed of a tractor and multiple trailers. As the path width changes with the different turning angles, as its path planning is complicated accordingly. On the base of presenting the kinematics equations of the tractor-trailer mobile robot, the maximum path width is deduced when the robot runs at the maximum turning angle. And this path width is named as equivalent size. Then genetic algorithmic is applied for the path planning method of tractor-trailer mobile robot. Firstly, the barriers are enlarged step by step with equivalent size. Then the vicinities around the barriers and the block neck zone are analyzed and labeled. The fitness function is defined according to both the path length and path width. Finally, the genetic algorithm is adopted to solve the whole path planning problem.

This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A ∗, and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed.

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.

Gene coding has always been an important and difficult problem in the course scheduling of genetic algorithm. Starting from the definition of the set and the limited conditions of the scheduling problem, this paper expounds the coding method of the scheduling problem of genetic algorithm in detail. The gene coding method in this paper is a decimal coding with special significance. The information of each course: teaching class, class time, course name, teaching teacher, classroom type and other information are encoded as chromosomes, and the coding method is more standardized and accurate. The crossover rate and variation rate of the designed genetic algorithm will automatically change with the change of fitness, and the self-adapting crossover rate and variation rate can make the algorithm fleetly get the globing greatest result. The fitness function is improved, and the traditional genetic algorithm is compared with the improved genetic algorithm. From the final results, It shows that the improved genetic algorithm is more excellent in efficiency and performance.

Mobile robot path planning using membrane evolutionary artificial potential field

A new genetic algorithm (GA), called intelligent GA, is proposed by the inclusion of a fractional factorial design in the crossover operator to determine the best combination of design variables. That is, in the proposed GA, the chromosomes of children are generated via an intelligent crossover process with factorial experiments after completing the selection of GA operators. This gene mating process differs from the traditional GA, where the child chromosome is generally generated via randomization. Accordingly, the traditional GA incorporating the technique of fractional factorial design into the crossover operator markedly enhances the performance in terms of evolutionary efficiency. Another GA, called Taguchi-GA, that incorporates the Taguchi orthogonal arrays into the crossover operator is also developed to compare the performance of the presented intelligent GA. Four representative test cases, including two multimodal functions, a nonlinear dynamic control function, and a profile fitting of a high lift airfoil are performed via the traditional GA, micro-GA, Taguchi-GA, and present GA to assess the capacity and efficiency of the proposed GA. The computational solutions of the convergence history and optimal objective function for the test cases demonstrate that the proposed intelligent GA outperforms the other three GAs. Moreover, the presented new intelligent GA and the other three GAs are applied to the aerodynamic optimization of two airplanes, namely, an advanced defense airplane and an F-16A fighter. The redesigned wing planform is obtained by computations made with an aerodynamic flow solver, and the present GA depicts the optimal swept angle, aspect ratio, and taper ratio. The drag distribution and convergence history show that the proposed intelligent GA has a high evolutionary efficiency when applied to practical engineering problems.

Aimed for the problems in traditional genetic algorithm of low search efficiency and easily falling into the local optimal solution, an improved genetic algorithm is proposed in this paper. So as to save the storage space, the simple one-dimensional code method is adopted to replace the method of complex two-dimensional coding. In the design of genetic operators, many operations such as crossover and mutation are redefined to avoid getting into the local optimum. Then the two fitness functions of collision-free path and the shortest distance are fused into one for the following genetic optimization. In the case of the same population parameters, 100 trials are respectively developed with the method of improved genetic algorithm and traditional genetic algorithm. Among them, the improved genetic algorithm to search the optimal path gets to 95 times, and the shortest path is 20.9706. Besides, the average searching time takes up 217ms. While the number of traditional method to search for the optimal path reaches up to 62 times, the shortest path can be 25.0711, and the average searching time needs 345ms. So compared to the tests results referred above, the improved genetic algorithm is more efficient and can get a better solution than the traditional genetic algorithm.

According to the characteristics of the online test paper composition, the genetic algorithm is preferred as the core algorithm. However, there are some problems in the traditional genetic algorithm when it is being used, such as low efficiency in the later search period and easy to fall into the local optimal solution. To solve this problem, this paper makes some optimizations for the traditional genetic algorithm's coding scheme, initial population, fitness function and genetic operator. After the improvement, the composing test paper module based on genetic algorithm was finally implemented in java language through seven parts.

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised.

Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.

This study presents a metaheuristic approach to coverage path planning for ground-based forest operations, focusing on minimizing path lengths for forest vehicles while considering terrain characteristics and vehicle parameters. Forest vehicles can usually tolerate higher pitch than roll angles, which makes them vulnerable to rollover. To mitigate that, this method utilizes a genetic algorithm to optimize the sequence of nodes, which are scattered over the site with equal spacing. The coverage path planner then calculates the Dubins path distance between every node in the fitness function, together with penalties for exceeding pitch, roll and soil moisture constraints for the vehicle. This ensures that the path planner tries to make the most traversable path as possible, while trying to minimize the driving distance. Two synthetic test sites resembling primitive challenging terrains, and one real site were utilized to theoretically evaluate the proposed method. The results show that aligning the node patterns with the critical slope headings, instead of having a straight pattern, had little effect on the path length. However, square grids can yield shorter paths across multiple runs, while triangular grids ensure consistent results in single runs. A two-hectare site took 43 minutes to calculate on average. This suggests that further development of the path planner could lead to significant improvements, enabling the management of sites larger than a few hundred nodes. However, the calculation time is justified for the reduced path length during deployment. The study presents a methodology that supports manual operators and establishes foundations for full autonomy.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

To optimize the container multimodal transportation path selection problem with differences in cargo time sensitivity, this paper introduces the concept of mixed time windows and establishes an integer programming model from the perspective of carriers. The objective is to minimize the total transportation cost while meeting the customer’s on-time delivery requirements. The model imposes hard time window constraints on cargoes with on-time delivery requirements and soft time window constraints on general cargoes. A bi-level genetic algorithm is employed to solve the model. A case study is conducted to demonstrate the effectiveness of the proposed approach. Experimental results show that the bi-level genetic algorithm’s convergence ability, efficiency, stability, and global search capabilities are far superior to those of traditional single-level genetic algorithms. This indicates that the use of multi-stage solution approaches can mitigate the shortcomings of genetic algorithms in dealing with high-dimensional problems. In addition, when analyzing the effects of time penalty intensity and time window on decision-making results, it is found that the more time-sensitive cargoes are more likely to be affected by the time window setting, and will tend to sacrifice costs to ensure that the time demand is met. At the same time an increase in demand for time-sensitive services is accompanied by an increase in operating costs. Accurate assessment and classification of time sensitivity of cargoes can effectively prevent wasting resources in unnecessary areas, and ultimately maximize decision outcomes and ensure the most efficient use of resources.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation