The proposed mFA can solve most of the problems that occur with pathplanning in 3Denvironment and find a path if one exists and in less time. it also adds a Safety rim around each obstacle to avoid the case when the distance to the obstacle that is against the robot's current position is smaller than the robot's step, then the robot will check the next position which passes through the obstacle's edge and move towards it ignoring the obstacle that’s in the way and avoid collision with obstacles. The planned path with proposed mFA is near optimal path; also it uses float values to indicate positions allowing for smoother and more accurate path representation. Figure 3 shows a path generated using the mFA in two different views.
Dynamicenvironment represents a revolution and important side in modern automation, it redound in many important diverse applications like, air and sea traffic control negotiating, freeway traffic, intelligent vehicles, automated assembly and automated wheel chairs. Because of existence the moving objects, the robot needs to make decision quickly for avoiding possibility collisions. This represents the mutual part among these applications. The ability to deal with moving objects is needful for the navigation of any factual robot. The range of applications of intelligent robots could be widely increased since it’s able to handle moving objects [5- 7]. There are three types of dynamicenvironment, where all information is regarding to the obstacles, i.e., motions, sizes, shapes, locations, etc., is completely known, the obstacle is said to be in a knowndynamicenvironment. As example for knownenvironment the automated production line. PartiallyknownDynamicEnvironment is the second type when not the all information about the obstacle are exist at the planning time. In this state, it needs to calculate the robot motion with according to insufficient information about the environment. It is important to know when the robot's mission is to complete a map of the environment by scanning the area or to scan a certain area to make sure if there is a new object is found in the area. Finally when there is nothing to know about the obstacle it’s called the totally unknown dynamicenvironment and this need intelligent ways to be used. As example for unknown and partiallyknownenvironment the other robots and humans in workspace .
Therefore, this paper presents an optimized RRT-A* pathplanningmethod based on morphological dila- tion (MD), goal-biased RRT, A* heuristic algorithm and cubic spline interpolation to compute safe and optimal path for autonomous mobile robots in par- tially known complex environment. A step size is usually used in goal-biased RRT for the generation of the RRT. In this paper, additional step-size is intro- duced to speed up the generation of the tree towards the goal based on the random sample value. In , A* heuristic function was applied at every iteration to select the nearest node during the generation of the tree which had high time cost in path computation. In this paper, the A* heuristic algorithm is used to optimize and obtain the shortest path after the path is generated using the modified goal-biased RRT. To provide safe and smooth path for feasible navigation, MD technique is used to inflate the obstacles before generating the path, and cubic spline interpolation (CSI) is used to smoothen the path. Path replanning is provided by generating new path from a current po- sition of the robot when a random obstacle obstructs its navigation path. ORRT-A* approach addresses the local minima problem reported with RRT-A*  and generates safe and optimal path with low time cost in partiallyknownenvironment.
This is a popular search-based optimization tool which followsthe principle of genetics and natural selection. Its application to the field of computerscience was presented first by Holland  in 1975. Nowadays, ithas wide application in all areas of science and technologyincluding robot navigation. Inthis approach, the population (different individuals characterizedby genes) must be allotted for the given problem and every memberof the population is assigned with a fitness value depending uponthe objective function. These individuals are selected as per theirfitness value and allowed to pass their genes to a new generation bycrossover. The mutation maintains the diversity in population andprevents premature convergence. Finally, the algorithm is terminatedif the population has converged. Although the GA is randomizedin nature to some extent, its performance is better as theycan exploit historical information as well when compared to arandom local search.
Abstract—Today’s mobile robots operate in natural industrial, commercial, or military environments and must interact with humans during the decision-making process. The natural environment is highly unstructured and often unknown, hence these robots must be able to process a large amount of information, and make planning and navigational decisions quickly. Autonomous mobilerobot have additional processing requirements where they have to collect, plan, and execute their planned route as well as performing tasks relevant to their specific missions. The pathplanning task is usually one of many important tasks that these robots must accomplish. Autonomous mobile applications require efficient and adaptive pathplanning approaches. A number of heuristic algorithms exist for producing optimal traverses given changing arc costs. But, most of analytical algorithms are focused on the path finding procedure in a knownenvironment and leave higher level functions, such as obstacle detection. The Cognitive Based Adaptive pathplanning (CBAPP) is an adaptive and cognitive based thinking system. And efficiency of the CBAPP was compared with analytical and a heuristic based algorithm results. Based on the simulation results, it was shown that CBAPP finds fast, efficient, and acceptable paths.
Coordination among interacting agents with a common group behavior is not only a recent engineering problem, but it is something that happens every day in nature. The interest in multi-agents systems has grown in the last decade, mostly because a group of collaborating agents can often deal with tasks that are difficult, or even impossible, to be accomplished by an individual agent. A team of agents typically provides flexibility, redundancy, and efficiency beyond what is possible with single agents. Having several agents deployed often means that they can be flexibly organized into groups performing tasks at different locations. Robustness is ensured by the numerous of the group if an agent fails the other can continue the task. Having many agents rather than one can allow performing tasks in a faster and more precise way. The multi-robot system is used for many applications due to robustness, potential to finish a task faster, varied and creative solution [16-17].
When searching for food, biological ants exhibit complex social behavior based on the hormones they deposited (called pheromones). Pheromones attract other ants and outline a path to the food source that other ants can follow. As more ants walk along the path, more pheromone is laid, and the chance that more ants will take the path increases. The shortest path to the food builds up the most pheromones because more ants can travel it in less amount of time. This phenomenon was first observed in the famous Double Bridge experiment : when given the choice between a short path and a long path to a food source, the ants consistently found the shortest path after a period of time. To prevent establishing a suboptimal path (when the solution is trapped into a local minimum), the pheromone also evaporates over time , thus reducing the change for other ants to take the path. On the other hand, the pheromone levels on the shortest path remain high because in this case, the pheromone deposit speed is faster than its evaporation speed.
implemented again with a record of the pheromone values that were updated after finding the best path in map1. Again in map3 the positions of the obstacles are changed and the implemented again without re-initializing it. Similarly, the positions of the obstacles is changed for every iteration and the algorithm is implemented until it reaches the maximum number of iterations. Sometimes, once the position of the obstacles is changed and the obstacle might not be in the grids that are a part of the best path, therefore it would not cause any changes to the path length. As we use the pheromone update rules, by the time the algorithm reaches the maximum iteration the best paths found at every iteration would contain a pheromone value. But there would be one path with the most concentration of pheromone, it is known as the near-optimal path. The length of the near-optimal path is the value of the convergence rate of the algorithm. Therefore, we note the iteration at which the algorithm has begun to converge and gives the same path length(which is the optimal path length) until the maximum iteration is reached. In our approach since we utilize all the iterations as well as all the pheromone values in order to narrow down to the optimal path value; we can say that we do not waste any of the resources or the parameters used.
This work presents a new proposal to solve the problem of pathplanning for mobile robots; it is based on TWPSS-ACOA to find the best route according to certain cost function. There are two core steps about pathplanning methods: environment modeling and planningalgorithm. So we firstly build the environment model of robot by EVB-CT--GM  , since this method was certified to be an effective method to overcome environment traps problem, and then TWPSS-ACOA proposed in  is used to adequately develop mutual cooperation ability between ants. But in view of that the TWPSS-ACOA has the defects of losing some feasible paths and even optimal paths, a new ants meeting judgment method that can judge if ants meet according to the kind of pheromones is proposed. And then a new method, which rationally distributes initial pheromone of ants, is given to speed up the convergence velocity of initial stages of ACO algorithm, since equivalent Manuscript received October 14, 2011.
A further variation of PRMs is the Rapidly Exploring Random Tree (RRT). Rather than randomly sampling the configuration space as a PRM does the planner begins at the start location and randomly expands a path, or tree, to cover the configuration space. The main focus is to build a roadmap in a fashion which draws the expansion of the connected paths toward the areas which have not been filled up yet. The planner pushes the search tree away from previously constructed vertices. This allows them to rapidly search large, high dimensional spaces. They are also well suited to the capture of dynamic or non-holonomic constraints, which with PRM methods have difficulty (although this capability is not critical for high level global pathplanning).
The experiment was carried out with various size of static environment, i.e. 128x128, 256x256 and 512x512, that consists of a goal point, three starting points and varying number of obstacles (L, T and Boxes objects). Initially, the outer boundary walls, inner walls and obstacles were fixed with high temperature values, whereas the goal point was set to very low temperature, and all other free spaces were set to zero temperature value. The experiments run on Intel Core 2 Duo CPU running at 1.83GHz speed with 1GB of RAM. The software code, now known as RobotPath Simulator (see Figure 3), was implemented in Delphi for very fast computation. Previously [16 - 19] the code was written in MatLab. The computation speed up was increased 5 folds with this Delphi implementation.
The ultimate goal of mobile robotics research is to endow the robots with high autonomous ability, of which navi- gation in an unknown environment is achieved by using on-line sensory information. First a correct environment model is usually needed and the robot can update the model by using sensory information. It is a key problem that the robot is able to map the environment automati- cally. A significant amount of research effort has been devoted to this area in the past decades such as probabil- ity grid , geometry algorithm , topology informa- tion  and the 3D information methods . The defi- ciency of geometry algorithm is that feature extraction is very difficult, especially in the complex environment. On the other hand, the topological method has the higher efficiency and smaller memory space. It is suitable for the large-scale work space. By this approach, main con- cern lays in finding the most effective way to map the environment while simultaneously localizing the robot’s position relative to the map. If just the accurate map were acquired, then the shortest path would be easily obtain- able from the occupancy grid map . However, to ac- quire such map in an unknown environment is not easy. The robot moves in the unfamiliar and non-stationary environment and doesn’t know any prior knowledge.
are shown in Fig. 7 (a). Fig. 7(b)-(c) show the environment with 20 static obstacles and one dynamic obstacle. In Fig. 7 (c), the three objectives, path length, smoothness and safety, are considered. The parameters of mMPSO are the same as above except for the population size 150 and m = 15. All the tests are executed for 100 independent runs. Table 3 shows the results. It can be seen from Tables 1-3 that the optimal solutions of mMPSO drop from 94 to 83, the elapsed time rises from 0.84 to 2.68 and the average generations vary from 27 to 59 as the number of cells increases from 16 × 16 to 64 × 64 and the static obstacles go up from 9 to 20. The elapsed time and average generations increase a little with the dynamic obstacle. To sum up, as the number of cells increases in accordance with 4 n (n = 1, 2, 3 . . .) values and the static obstacles double, the increase in the elapsed time is quite small, as opposed to an exponential growth in cells number. mMPSO maintains good search capability to find the optimal solution in both static and dynamic environments, which indicates mMPSO has good adaptability to MR3P under complex environments.
A multi agents system has a global goal or mission that it has to achieve; this goal can be decomposed into sub goals following the given criteria: simple goals are defined as goals that are realized by one single agent; replication is used within this group because agents are considered as too important. A complex goal is defined as the one which needs more than one agent to be achieved. The existing of two or more agents in cooperation to realize one complex goal gives us the idea of using exception handling within this type of groups. So, our approach uses replication an exception handling separately in different groups but they are combined through one multi agents system.
with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small time- complexity in comparison to its classical counterpart. Further, the proposed algorithm stores the Q-value for the best possible action at a state, and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for path-planning application of mobile robots outperforms both the classical and extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90 o turns required. Reduction in 90 o turnings minimizes the energy consumption, and thus has importance in robotics literature.
Many researches have investigated, during the last decades, the guidance of land autonomous vehicles, underseas robots, manipulators and walking machines. Many experiments have been carried out on real robots (wheeled mobile robots, and AUV) and on simulated ones , . A real-time obstacle avoidance algorithm coupled with path following is studied and implemented in this paper.
Dijkstra algorithm is widely used in finding the shortest path from a starting point to other vertices [12-14] . The advantage of the algorithm lies in finding out the vertex closest to the starting point, automatically taking it as the new starting point (knee point), and updating the original path information simultaneously. In this paper, an improved Dijkstra algorithm  can find out the minimum distance matrix between two points. The method to solve this problem can be divided into three steps. The main computation procedure of Dijkstra algorithm is described as follows.
ABSTRACT: Image segmentation is a fundamental technique in image processing systems such as a medical computer-aided- diagnosis system. We previously proposed a discrete-time oscillator network system with a degradation (posterization) system for dynamic image segmentation. This system can extract image regions with similar pixel values (connected compo- nents) from a given gray-scale image and exhibit extracted image regions in a time series. The dynamics of the proposed system is described by ordinary difference equations. In spite of discrete-time system, our oscillator model can generate oscillatory responses like relaxation oscillations observed in ordinary differential equations. Dynamic image segmentation for a gray-scale image is performed according to posterization of pixel values and the synchronization of oscillatory re- sponses from discrete-time oscillators. Since numerical integration is unnecessary to work the proposed system, its process- ing speed is significantly faster than that of another dynamic image-segmentation system consisting of ordinary differential equations. In this paper, for further speed-up of dynamic image-segmentation processing, we presented an implementation of the proposed system into a graphics processing unit.
of autonomous mobilerobot, an attempt has been made to design an intelligent control system and routing for an autonomous mobilerobot in a dynamicenvironment. Also, by using input feedback linearization of the fuzzy system, we provide a new approach to control of mobile robots in dynamic environments. The proposed system is designed in such a way that our intelligent robot determines its own motion plan autonomously, by considering the position and the direction of moving obstacle, and also considering the position of target point. In this motion plan, by using fuzzy inference system and without human intervention, the robot starts to move from beginning point and estimates the position of obstacle sequentially by Kalman filter at any moment. with regard to direction of obstacle movement, the robot selects a direction to the target point in the way that it reaches target point without collision with moving obstacle. In this plan, the force applied to robot wheels is also determined according to the distance between robot and target by fuzzy inference system. So our _______________________________