Optimization of Robotic Mobile Agent Navigation

Procedia Economics and Finance, 2012

Many difficult problem solving require computational intelligence. One of the major directions in artificial intelligence consists in the development of efficient computational intelligence algorithms, like: evolutionary algorithms, and neural networks. Systems, that operate in isolation or cooperate with each other, like mobile robots could use computational intelligence algorithms for different problems/tasks solving, however in their behavior could emerge an intelligence called system s intelligence, intelligence of a system. The traveling salesman problem TSP has a large application area. It is a well-known business problem. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic Raja, 2012. It also has a wide range of applicability in the mobile robotic agent path planning optimization. In this paper a mobile robotic agent path planning will be discussed, using unsupervised neural networks for the TSP solving, and from the TSP results the finding of a closely optimal path between two points in the agent working area. In the paper a modification of the criteria function of the winner neuron selection will also be presented. At the end of the paper measurement results will be presented.

InTech eBooks, 2011

World Academy of Research in Science and Engineering, 2020

A mobile robot is a mechatronic system that can facilitate human labor. These systems are widely used in various fields of production. A key element of a mobile robot is its navigation system. For the successful use of the navigation system of the mobile robot and its subsequent efficient operation, it is necessary to plan the route. This will avoid errors in the movement of the robot, solve the tasks. Among the main tasks of route planning for a mobile robot, as a rule, are distinguished: building a map of the robot's motion environment, and adjusting the robot's motion path. The article discusses the main points of such a generalization, provides algorithms for solving specific problems.

2013

Mobile robots are vital for automation industries, surveillance and mapping, hazardous operation like nuclear plants, landmine detection etc. The path of such robots is controlled by a navigational algorithm. Several algorithm have been proposed and tried out for navigation of an autonomous mobile robot (AMR) around the globe .Some of these determine the path which is feasible to reach the destination without collision, while other also tries to optimize .Key parameters of the navigation are distance and time (either or both) to reach the destination or cost of reaching the destination. The prevalent algorithm have used various technique like fuzzy logic, genetic algorithm, artificial neural network, dynamic programming, potential field method, bug algorithm, ant colony optimization etc. Many others have developed the specific algorithms in evolutionary manner stage by stage through various trials. This also includes a number of heuristic based algorithms. This article describes the...

Mobile robot path planning problem is an important combinational content of artificial intelligence and robotics. Its mission is to be independently movement from the starting point to the target point make robots in their work environment while satisfying certain constraints. Constraint conditions are as follows: not a collision with known and unknown obstacles, as far as possible away from the obstacle, sports the shortest path, the shortest time, robot-consuming energy minimization and so on. In essence, the mobile robot path planning problem can be seen as a conditional constraint optimization problem. To overcome this problem, ant colony optimization algorithm is used.

ArXiv, 2012

The paper presents the electronic design and motion planning of a robot based on decision making regarding its straight motion and precise turn using Artificial Neural Network (ANN). The ANN helps in learning of robot so that it performs motion autonomously. The weights calculated are implemented in microcontroller. The performance has been tested to be excellent.

The purpose of this paper is to present a new approach for path planing of a mobile robot in static outdoor environments. A simple sensor model is developed for fast acquisition of environment information. The obstacle avoidance system is based on a micro-controller interfaced with multiple ultrasonic transducers with a rotating motor. Using sonar readings and environment knowledge, a local map based on weight evaluation function is built for the robot path planing. The path planner finds the local optimal path using the A* search algorithm. The robot is trained to learn a goal-directed task under adequate supervision. The simulation experiments show that a robot, utilizing our neural network scheme, can learn tasks of obstacle avoidance in the work space of a certain geometrical complexity. The result shows that the proposed algorithm can be efficiently implemented in an outdoor environment.

WSEAS TRANSACTIONS ON SYSTEMS

This survey paper presents a collection of the most important algorithms for the well-known Traveling Salesman Problem (TSP) using Self-Organizing Maps (SOM). Each one of the presented models is characterized by its own features and advantages. The modes are compared to each other to find their differences and similarities. The models are classified in two basic categories, namely the enriched and hybrid models. For each model we present information regarding its performance, the required number of iterations, as well as the number of neurons that are capable of solving the TSP problem. Based on the experimental results, the best model is identified for different occasions. The paper is a good starting point for anyone who is interested in solving TSP with SOM and desires to grasp a lot about this renowned problem.

Advances in Science, Technology and Engineering Systems Journal

The emerging trend of modern industry automation requires intelligence to be embedded into mobile robot for ensuring optimal or near-optimal solutions to execute certain task. This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. The purpose of this paper is to review the mobile robots path planning problem, optimization criteria and various methodologies reported in the literature for global and local mobile robot path planning. In this paper, commonly use classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (AFP), and heuristics approaches such as genetic algorithm (GA), particle swarm optimization (PSO) approach and ant colony optimization (ACO) method are considered. It is observed that when it comes to dynamic environment where most of the information are unknown to the mobile robots before starting, heuristics approaches are more popular and widely used compared to classical approaches since it can handle uncertainty, interact with objects and making quick decision. Finally, few suggestions for future research work in this field are addressed at the end of this paper.

2014

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue ...