Abstract: Pathplanning and real-time obstacle avoidance is the key technologies of mobilerobot intelligence. But the efficiency of the globalpathplanning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a globaldynamicpathplanningmethod based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuris- tic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the globaldynamicpathplanning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamicpath is planning. On the basis of ensuring the optimal global optimization of the planningpath, it is improved that the smoothness of the planningpath and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional pathplanning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the newpath can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobilerobot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned globalpath is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for globaldynamicpathplanning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamicpathplanning application of mobilerobot.
Figure 12 a,b respectively show the performances of the traditional VFH* pathplanning algorithm and the DWA algorithm in the scene where virtual obstacles were suddenly added. It can be seen that neither of the two methods could complete the experiment well. When using the DWA method, it was difficult to update the path in time, so after adding virtual obstacles to the map, the mobilerobot did not correct the path to avoid the obstacles, but still moved according to the original path, failing to reach the target point. Compared with the traditional VFH* method, the main reason why the improved VFH* has a better effect is that the new cost function fully considers the characteristics and requirements of the system. Since the platform used was OMR, the unaffected part of the original function was removed, and the coefficient of the function was modified to make the algorithm more sensitive to the virtual obstacles suddenly added in the distance and to adjust the path in time.
system, on the basis of nonlinear control theory, we constructed a control system  for preventing the generation of a fixed point that corresponds to non-oscillatory responses and is unsuitable for dynamic image segmentation. Moreover, we proposed a method  to identify target image regions for a given binary (black and white) image using bifurcation theory. This supports that we know the number of image regions to be segmented before the process of image segmentation.
In an unknown environment, the robot can only obtain obstacle information within the robot’s view through the sensor, and constantly utilizes and dynamically feedbacks the information within the robot’s view to plan a local navigation path. Rolling window is an effective method for robotpathplanning in unknown environments.  proposed an adaptive artificial potential field method combined with the rolling window method to solve the local-minima problem and plan the robotpath in a dynamic environment.  proposed a newrobotpath rolling planning algorithm which maps the goal node to a sub-goal node nearby the boundary of robot’ view.  presented a novel and effective robotpathplanningmethod for dynamic unknown environments. In each iteration, the robot planned a local navigation path based on an improved ant algorithm. The idea of the traditional rolling window method [18, 19, 20] is relatively simple, lack of intelligence and only incorporate the global target information into the sub-target selection, instead of combining it with the holistic environment information in the robot’s view. The traditional rolling windows approach maps the sub-target point to the boundary of the planning window, and the sub-target point guides the robot to the target point. As the purpose of guiding the robot towards the target point is too intensified, the planned path is often subject to detours and is not a globally optimal one. Meanwhile, it is very easy to fall into local deadlock and oscillation.
Z. Guesssoum and al  introduce an automatic and dynamic replication mechanism. They evaluate dynamically the criticality of an agent using various data such as: time processing, the role taken by an agent in the system… This mechanism uses the interdependence graph and the agent through this approach has to interrupt its work in order to evaluate the criticality A. Almeida et al  introduce a methodology for replication based on the concept of agent plan of actions each agent of the system evaluates its criticality according to a method proposed then agents that are more critical will be replicated using the framework DARX . This approach treats the case of cooperative systems and it obliges agents to interrupt their executions in order to evaluate their criticality.
Abstract. Pathplanning is an important aspect of mobilerobot. This paper mainly studies the globalpathplanning with multiple sub-path targets. Firstly, this paper build two initial distance matrix, then construct a minimum distance matrix by Dijkstra algorithm. Finally, the paper determine the destination nodes which have all minimum path. The pathplanningmethod can be used to some service robots, including: family service robots, medical robots, industrial robots.
Parking control system has recently attracted considerable attention in the control community. The biggest problem people face while driving is not actually driving the car, but parking it. Many researchers have tried to find the optimized solution in different ways to alleviate the driver’s parking burden. There are many types of parking method: bay parking (90 degree), slide parking (45 or 65 degree), and parallel parking (180 degree). Among them, parallel parking is the most difficult parking method even experience driver may cause collision. Parallel parking means on a road where cars are parked in front of each other, bumper to bumper, length wise and there exists a gap, the car can be parked between it, in parallel . A rule based parallel parking system for a prototype mobilerobot has been developed in this research. This kind of system is designed to improve comfort and security of driving in constrained environments where a lot of attentions and experiences are needed to maneuver the vehicle. Since the task of parallel parking requires many repeated maneuvers in a very limited space, its nature is different from the normal driving in a huge environment. There are two different approaches to solve the parking problem. The first one, the trajectory planning approach, needs a mapping and auto-localization algorithms. In addition to this, it generates paths by using the dynamic and kinematics models of the vehicle and vehicle constraints. Then, it controls the vehicle velocity and the steering angle to ensure a position in the mapped environment [2-6]. On the other hand, the second approach tries to mimic the skills of an experienced driver by using Artificial Intelligence techniques such as finite automaton, fuzzy logic (e.g. Zhao and Collins Jr ) or artificial neural networks (e.g. Jenkins and Yuhas . These methods do not need any pathplanning and their associated tracking control but they are limited to the experience of human
Now, if we can perform localization helping a mobilerobot, which is aware of its place (for example, using GPS), we can again decrease the consumed energy and cost. In this way, the plan- ning of mobilerobot is regarded as a basic and fundamental problem because the accuracy of localization is always influenced by robot move- ment path. In this paper, pathplanning of mobilerobot and nodes localization is studied that uses the power of the received signal (RSSI). This method uses suggested algorithm by sichitiu and others to obtain the place of nodes from their trav- elled distance. In the rest of the paper, first we discuss previous works in assessing the distance and nodes localizations and the propose a brief description by sichitiu and others, then we study graph theory and spanning tree for path design of mobilerobot and suggest travelled distance al- gorithm with the names of MMNF and LDF for spanning tree and these algorithms works better and less consuming than BRF and BTG. Finally, we discuss simulation scenarios and conclusions and finally we conclude the paper.
In recent years, many scholars have also applied some methods of incremental learning to the robot environ- ment map. Oishi, T. et al. designs the architecture of a self creating and organizing neural network for workspa- ce recognition and navigation of an autonomous mobilerobot. Methods of pathplanning and navigation based on a topological map created by learning are also proposed. The proposed architecture was tested by simulation of an autonomous mobilerobot with eight sonar sensors, and it was demonstrated that the architecture is useful for the purpose . Gyu-jong Choi used the topological form to build the environment map . Y. Zhuang et al. had maked use of the geometry-topoloty hybrid approach . A. Kawewong et al. used an approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN) because it is suitable to noisy environment and is easily implemented . Ruan et al. presented an algorithm of Dynamic Growing Self-orga- nizing (DGSOM) using in building environment . It is simple and effective but the static value of Growing Threshold (GT) is used which can not realize mapping quickly.
technique. Therefore, the proper selection of the navigationaltechnique is the most important step in the pathplanning of a robotwhen working in a simple and complex environment. At present,many techniques have been developed by various researchers inthe field of mobilerobot navigation and it is the most researchedtopic of today. Mobilerobot navigation is classified into three categories:global navigation, local navigation and personal navigation.The capability to define the position of elements in theenvironment with respect to the reference axis, and to stir towardsthe pre-decided goal, is global navigation. Local navigation dealswith the identification of the dynamic conditions of the environmentand the establishment of positional relationships amongvarious elements. To handle the various elements of the environmentrelative to each other, by considering their position, is personalnavigation. The basic steps involved in the functioning of therobot  are presented in Fig. 1.
A mobilerobot can localize itself using two different classes of on-board sensors: pro-prioceptive sensors and exteroceptive sensors. Proprioceptive sensors, such as encoders or inertial measurement units (IMUs), measure the motion of the robot, acquiring data that can be integrated to estimate relative robot displacement. This method of localization is called odometry, or dead reckoning, and when used alone, the integrated error in global position grows without bound over time.
Connolly et al.  and Akishita et al. , both of them developed independently a globalmethod that generates smooth path by using solutions to Laplace’s equations, where the potential fields are computed in a global manner over the entire region. In , Connolly & Gruppen show that harmonic functions have a number of properties useful in robotics. Then, Sasaki  computed harmonic functions using numerical technique to be used for mobilerobot navigation. Waydo & Murray  used stream functions that are similar to harmonic functions to generate motion planning for a vehicle. Daily & Bevly  used harmonic potential field for pathplanning of high speed vehicles. Garrido et al.  used finite elements to compute harmonic functions for robotic motion. More recently, Szulczyński et al.  employed harmonic potential functions for real-time obstacle avoidance.
Traditional ant colony algorithm has the shortcomings of slow convergence speed and easily trapped into the local optimum. To overcome these problems, the paper improves the transition rule of node state, improves the heuristic function and the way to update pheromone. The algorithm was simulated in the different complexity of grid environments and can always find the ideal path. It shows the efficiency and feasibility of the improved algorithm. In addition, comparing to the traditional ant colony algorithm, the improved algorithm in this paper can find the optimal path by a fewer iterations in the complex environments. It shows that the improved algorithm has the feasibility and superiority. The paper mainly researches globalpathplanning for mobilerobot in a static environment without considering the existence of a finite number of dynamic obstacles. This is the shortage of the paper and the focus of future research.
The main contribution of this study is to provide pathplanning alternatives for mobile robots using cellular learning automata. Existing pathplanning methods for mobile robots implemented in ROS (Robot Operating System)  were designed using Dijkstra’s algorithm and the A* algorithm, where the pathplanning is computed step by step between two nodes of an irregular mesh. Our approach extends the existing method by exploiting the regularity of cellular automata in which the distance between two adjacent cells is equal. Furthermore, we use cellular learning automata to optimize the path generated in the first stage.
In , the authors presented a modified ACO algorithm to solve the globalpathplanning problem in an indoor environment for a UAV. The workspace of the UAV is modeled as 3-dimenstional grid map. To overcome the shortcomings of the conventional ACO, the authors proposed a modified pheromone update rule: they proposed to update the quantity of pheromone only on the best path generated after an iteration of the algorithm. They also limited the quantity of pheromone to solve the problem of premature convergence of ACO. To evaluate the robotpath, they added a climbing parameter in addition to the path cost, they claimed that this parameter will help the robot to choose the best direction and bypass obstacles. It was demonstrated throw simulation that the algorithm could produce good quality of solution in 10*10 grid map. In , Ganganath et al. proposed an off-line path planner for a mobilerobot based on Ant Colony Optimization algorithm with Gaussian functions (ACO-2-Gauss). The workspace of the mobilerobot is a 3D hilly landscape. Unlike ordinary ACO algorithms, the proposed path planner provides the ants an extra ï ˇn ´Cexibility in making routing decisions: an ant is allowed to select any point on the circumference of a circle with a radius of R as its next position. Each ant will select a set of circles to move toward the goal position. The pheromone is distributed on the center of the different circles as a 2D Gaussian function. In their simulation study, the authors compared the proposed algorithm against a preceding version: ACO-Gauss, it was demonstrated that the quality of solution of ACO-2-Gauss was improved as compared to ACO-Gauss path planner.
Abstract. In order to solve the problem of globalpathplanning for mobilerobot, a global optimization algorithm based on genetic bee colony algorithm is proposed. This algorithm is combining the global optimization strategy of the genetic algorithm and artificial bee colony algorithm. The improved crossover and mutation operator are introduced into the algorithm to increase the diversity of food source, effectively avoiding the swarm into local optimum, improving the ability of search for the food source. Besides, a strategy of adaptive selection has ability to search the optimal food source. A large number of the experiment and comparative analysis are carried out by using grid method in this paper. Experimental results show that this method has high precision and fast convergence speed, and it is an effective method for pathplanning.
The development of computer technology promotes the progress of intelligent robots. Modern intelligent robots can plan and make decisions through different kinds of sensors, so as to achieve different functional goals . Intelligent robots have been used in various industries. The birth of this new intelligent tool has constantly changed the traditional production mode of people, which greatly improving human productivity and redu- cing human production risks . Among these robots, mobile robots are widely used. Mobile robots use sen- sors to identify the environment and locate their own positions . The sensors of the robot can be divided into internal and external sensors. The external sensors include laser range finder, fiber optic sensor, and ultra- sonic sensor. Internal sensors include gyroscopes and odometers. . However, there is a problem of inaccur- ate information in the process of data acquisition for sensors in mobile robots. The environment of mobile ro- bots is also a dynamic environment, and it is also a mov- ing object itself. Therefore, the traditional sensor technology of mobile robots has been difficult to meet the accuracy and stability requirements of mobile robots . Foreign robotics and computer technology develop- ment earlier in Japan, Europe, and other developed countries on mobilerobot research and production has
The pathplanning is the basic element of the mobilerobot. The so-called mobilerobotpathplanning is searching for an optimal or sub-optimal collision-free path connecting the initial position and target position. According to the environmental information that collected by the robot, the pathplanning can be divided into two categories: pathplanning with fully known global environmental information and completely unknown or partially unknown environmental information . The common methods that have been used, such as grid method and artificial potential field method and so on, but all these algorithms have limitations. The shortcomings of the grid method are when the space increases, it will lead to dramatic increase in storage space that required, and decision is made slowly. The structure of artificial potential field is simple, convenient for real-time control, but may generate minimal path and oscillation in front of obstacles.
Find-path problem or Pathplanning is popular in robotics since it has an essential role in the navigation of autonomous mobile robots. Accurate pathplanning is very important for the robot to be able to track an optimal path between start position and goal position without collision with obstacles. According to , there are two main types of pathplanning: globalpathplanning and local pathplanning. Globalpathplanning needs all terrain to be static. In addition, there should be a complete awareness about the environment. In contrast, the implementation of the local pathplanning is while the robot is moving. Therefore, the local pathplanning has the ability to produce a newpath when there is a change in the environment. For this project the local pathplanning will be used since the robot needs to check if there is an obstacle within its path to avoid it by change its path .
In this section, we review some of researches in the field of mobilerobotpathplanning methods. Khatib’s work  defined a potential field on the configuration space with a minimum at the target point and potential hills at all obstacles. The mobilerobot in the potential field is attracted to the goal point while being repelled by obstacles in the environment. The robot should follow the gradient of the total artificial potential to goal point while avoiding collisions with obstacles. Chengqing et al.  have introduced a navigation method, which combined virtual obstacle concept with a potential‐field‐based method to maneuver cylindrical mobile robots in unknown environments. Simulation by computer and experiments of their method illustrates accepted performance and ability to solve the local minima problem related with potential field method. Evolutionary Artificial Potential Field (EAPF) for real‐time robotpathplanning has proposed by Vadakkepat et al. . Combination between genetic algorithm and the artificial potential field method is introduced to derive optimal potential field functions. With the proposed method in this work, the mobilerobot can avoid static and dynamic obstacles. A new potential function is proposed by Ge and Cui that function take into consideration of dynamic environments that contain moving obstacles and goals. In this work positions and velocities of robots, obstacles, and goals are considered in the functions of potential field . GNRON problem (goals non-reachable with obstacle nearby), which is a common drawback in most potential field methods, is identified by Ge and Cui  and Volpe and Khosla . GNRON problem can be solved using their proposed potential field method. Detailed studies of these local potential methods, their characteristics and their limitations were discussed in .