Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster

Semi-open multi-distribution center path planning with time windows

first_pagesettingsOrder Article Reprints Open AccessArticle Semi-Open Multi-Distribution Center Path Planning with Time Windows by Qin Song 1,2ORCID 1 School of Engineering, Cardiff University, Cardiff CF24 3AA, UK 2 School of Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China Sustainability 2023, 15(6), 4800; https://doi.org/10.3390/su15064800 Received: 6 January 2023 / Revised: 4 March 2023 / Accepted: 6 March 2023 / Published: 8 March 2023 Download Browse Figures Versions Notes Abstract A well-planned robot dispatching platform reduces costs and increases efficiency for companies while also reducing carbon emissions and achieving sustainable development. At the moment, the solution to the difficulty of warehouse logistics is use of multiple distribution centers with autonomous mobile robots (AMR). To solve this problem, this paper establishes a semi-closed model of multiple distribution centers, considering the number of cycles and the number of vehicles. An improved ant colony algorithm is proposed to improve the heuristic function based on the node distance relationship to improve the quality of path search. Dynamic variable pheromone concentration and volatility factors are set to accelerate the convergence speed of the algorithm while effectively reducing the problem of the premature algorithm. The traditional ant colony algorithm and the improved ant colony algorithm are used to solve the established model. In addition, the results show that the traditional ant colony algorithm has a certain rate of dominance in the single-day cost of the closed distribution model, but the overall comprehensive cost is lower than that of the improved ant colony algorithm. The single-day cost of the semi-open multi-distribution center logistics and distribution model is lower than that of the closed multi-distribution center logistics and distribution model, and the 7 day average cost is reduced by 12%. The improved ant colony algorithm can save about 119 kWh of electricity under the same target volume requirement, which achieves the company’s goals of cost reduction and increased efficiency, as well as green and sustainable development

Trajectory Planning Algorithms in Two-Dimensional Environment with Obstacles

This article proposes algorithms for planning and controlling the movement of a mobile robot in a two-dimensional stationary environment with obstacles. The task is to reduce the length of the planned path, take into account the dynamic constraints of the robot and obtain a smooth trajectory. To take into account the dynamic constraints of the mobile robot, virtual obstacles are added to the map to cover the unfeasible sectors of the movement. This way of accounting for dynamic constraints allows the use of map-oriented methods without increasing their complexity. An improved version of the rapidly exploring random tree algorithm (multi-parent nodes RRT – MPN-RRT) is proposed as a global planning algorithm. Several parent nodes decrease the length of the planned path in comprise with the original one-node version of RRT. The shortest path on the constructed graph is found using the ant colony optimization algorithm. It is shown that the use of two-parent nodes can reduce the average path length for an urban environment with a low building density. To solve the problem of slow convergence of algorithms based on random search and path smoothing, the RRT algorithm is supplemented with a local optimization algorithm. The RRT algorithm searches for a global path, which is smoothed and optimized by an iterative local algorithm. The lower-level control algorithms developed in this article automatically decrease the robot’s velocity when approaching obstacles or turning. The overall efficiency of the developed algorithms is demonstrated by numerical simulation methods using a large number of experiments

THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE

Simulation of identifying shortest path walkway in library by using ant colony optimization

A research is proposed based on Ant Colony Optimization for solving the shortest path problem in library.This is a research that the algorithm is aim to implement on a robot. The robot is used to walk around in the library to collect books from all the tables and put on book shelves.However, command prompt window will use to shows the result which is the shortest path.People nowadays are more concern about the efficiency of work,this may happen in library as well. Therefore,by determining the shortest path will help in reducing the time consume problem.This project is developed by starting with designing the workflow diagram as well as the design of the output interface.The work flow is the guide for the process of development.In between,Heuristic Approach is used to determine the entire possible paths at first,then Ant Colony Optimization algorithm will be implemented to search for the final and the shortest path. The system is used to be error free and the algorithm can effectively solve the shortest path problem

Robot Path Planning with IGA-MMAS and MMAS-IGA

Path Planning of mobile robots is one of the essential tasks in robotic research and studies with intelligent technologies. It helps in determining the path from a source to the destination. It has extended its roots from classic approaches to further improvements over time, such as evolutionary approaches. Ant Colony Optimization (ACO) and Genetic algorithm are well known evolutionary approaches in effective path planning. This research work focuses on the Max-Min Ant System (MMAS) derived from the ACO evolutionary approach of Ant System (AS) and Improved Genetic Algorithm (IGA) which is efficient over the classical Genetic Algorithm. In-order to study robot path planning two methods are combined in this research work combining MMAS and IGA as two-hybrid methods MMAS-IGA and IGA-MMAS . The results of the two-hybrid methods will be deriving the near optimal solution, demonstrated in the experimental study of this work. Grid maps are used for simulating the robot path planning environment which is modeled using the grid method. Genetic operators of IGA are combined with MMAS for the enhancement of the overall result of the methods IGA-MMAS and MMAS-IGA. The effectiveness of these two methods will be determined in the simulation modeled using MATLAB environment. The experimental results of these methods are done in a static environment and the results of MMAS-IGA and IGA-MMAS are compared to the path planning method GA-ACO

Survey on Path Planning of Mobile Robot with Multi Algorithms

Sensible practical environment for path and continuous motion preparation problems usually involves various operational areas coupled with indoor usage comprising of multiple apartments, corridors, a few doors and several static and active obstacles in between. The disintegration of this system into limited areas or regions indicates an effect on the fun preparation of appropriate pathways in a complex setting. Many algorithms are designed to solve problems with narrow passages and with optimal solution for more than one field. Independent mobile robot gadget would have felt the stability of its abilities, the steadfastness and the question of resilience with the project and the implementation of an innovative as well as an efficient plan with the best approach. Navigation algorithms reaching a certain sophistication in the field of autonomous mobile robot, which ensures that most work now focuses on more specialized activities such as efficient route planning and navigation across complex environments. Adaptive way to prepare and maneuver needs to establish learning thresholds, legislation to identify areas and to specify planned requirements of the library. The aim of this survey is studying many algorithms to view the advantage and disadvantage for each method then can use optimal method depended on this study

Planificación de trayectorias usando metaheurísticas

In this work, a comparison between two metaheuristic methods to solve the path planning problem is presented. These methods are 1) Artificial ant colony and 2) Artificial bee colony. The following metrics are used to evaluate these implementations: 1) Path length and 2) Execution time. The comparison was tested using ten maps obtained from the University of Prague Department of Intelligent Cybernetics and the Mobil Robotics Group. Several runs were carried out to find the best algorithm parameters and get the best algorithm for the route planning task. The best algorithm was the artificial bee colony. These evaluations were visualized using the VPython package; here, a differential mobile robot was simulated to follow the trajectory calculated by the best algorithm. This simulation made it possible to observe that the robot makes the correct trajectory from the starting point to the objective point in each evaluated map.En este trabajo se presenta una comparación entre dos métodos metaheurísticos para resolver problemas de planificación de rutas. Estos métodos son: 1) Colonia de hormigas artificiales y 2) Colonia de abejas artificiales. Para evaluar estas implementaciones, se utilizan las siguientes métricas: 1) Longitud de ruta y 2) Tiempo de ejecución. El comparativo se probó utilizando diez mapas obtenidos del Departamento de Cibernética Inteligente y Mobil Robotics Group de la Universidad de Praga. Se realizaron varias ejecuciones con el objetivo de encontrar los mejores parámetros de los algoritmos y obtener el mejor algoritmo para la tarea de planificación de ruta. El mejor algoritmo fue la colonia de abejas artificiales. Estas evaluaciones se visualizaron utilizando el paquete VPython, aquí se simuló un robot móvil diferencial para seguir la trayectoria calculada por el mejor algoritmo. A partir de esta simulación fue posible observar que el robot realiza la trayectoria correcta desde el punto de inicio hasta el punto objetivo en cada uno de los mapas evaluados