Abstract A practical pathplanning method for a multiplemobilerobotsystem (MMRS) requires handling both the collision-free constraint and the kinematic constraint of real robots, the latter of which has to date been neglected by most pathplanning methods. In this paper, we present a practical cooperativepathplanningalgorithm for MMRS in a dynamicenvironment. First, each robot uses an analytical method to plan an obstacle-avoidance path. Then, a distributed prioritized scheme is introduced to realize cooperativepathplanning. In the scheme, each robot calculates a priority value according to its situation at each instant in time, which will determine the robot’s priority. Higher-priority robots can ignore lower-priority robots, whereas lower-priority robots should avoid collisions with higher-priority robots. To minimize the path length for MMRS, a least path length constraint is added. The priority value is also calculated by a path cost function that takes the path length into consideration. Unlike other priority methods, the algorithm proposed is not time consuming; therefore, it is suitable for dynamic environments. Simulation results are presented to verify the effectiveness of the proposed algorithm.
In this paper, A* pathplanningalgorithm has been represented for a mobilerobot to be able to follow a constructed path from its current position to a specified goal within its environment. To ensure that the mobilerobot follow the constructed path by pathplanningalgorithm, a motion control algorithm has been built. In the same time, to detect static obstacles and avoid collision with them, an obstacle detection algorithm has been used as a final algorithm that will be used as a part of the whole system to give the robot the ability to move from its initial known position to a specific goal in an optimum way.
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 global pathplanning 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.
(III) The establishment of an environment model is the foundation of MR3P and decides the environment feature (static or dynamic), and how to choose an evaluation method and an optimization approach to implement the pathplanning for a mobilerobot. There are three main environment models: vector (obstacles represented by polygons), grid (occupancy cell) and graph (Voronoi diagram or visibility graph). As compared with vector and graph, grid has the advantages of simple and flexible. This study uses a grid environment. There are two ways of representing a grid-based environment. One is a X-Y coordinates plane  and the other is an orderly numbered grid, which has been widely used. We adopt the latter approach, in which a square environment is evenly divided into a certain number of squares, i.e., the x-axis and y-axis are divided equally into m parts, thus, we get m × m grids, where one or more grids are used to represent the obstacles. An example of the 7 × 7 grids is shown in Fig. 1(a), where the grid map is encoded by using Matlab and the grey shadow grids represent obstacles.
SLAM is an operation, employing an independent robot or an autonomous vehicle, develop or construct a path or track for its climatic or environmental conditions on an uncertain or unclear region on land . At the same time, the autonomous vehicle limits its path or range of area by using the data retrieved before through the route map. At the end of the day, the self-sufficient vehicle attempts to do both mapping and limitation of its track or path simultaneously ,. The EKF SLAM approach to slam is to develop a filtering process for the system . Our work operates on the two-dimensional SLAM in a three-dimensional environment . It adds the carrying believe of having obstacles or having landmarks that are present in the ground plane. The system is modelled in a discrete-time system, with a variable state denoted by a vector known as state vector. The variables in the state vector can change over the motion of the vehicle, hence the system is dynamic. The EKF-SLAM system is like the estimation of a linear dynamicsystem.
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.
Aim at this problem the dynamic window method is used in this paper. It has a good obstacle avoidance capability in the dynamicenvironment . But this method does not satisfy the global optimal pathplanning. The node-based A* algorithm is suitable for global pathplanning. It has the advantages of simple data, small calculation compared to the biological inspired GA, PSO, etc  . But when the environmental space increases, it needs large storage space, low efficiency, and poor real-time decision-making . So the path curvature of the traditional A* algorithm is discontinuous. It will cause the movement parameters jump at the turning point. These are not conducive to the robot control problems . Aim at these problems the algorithm is improved. It makes the path smoother on the basis of the overall optimization of the planningpath . so a global dynamicpathplanning method is proposed that it is combining improved A* algorithm and dynamic window method in the paper. It is proposed the evaluation function taking into account the global optimal path. And the real-time pathplanning is carried out by using dynamic window method based on the evaluation function. It would make the path smoother on the basis of ensuring the overall optimization of the planningpath. At the same time it improves the local obstacle avoidance.
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 known environment 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.
Potential field is one of the most common methods for pathplanning. The popularity of this method is due to its simplicity for implementation. This method was introduced by  and then improved for real time implementations in . First, potential field approach was suggested for navigation in static environment , , , . However, the real world is not stationary, and the robot moves in dynamicenvironment and encounters dynamic obstacles like moving humans. Researchers started to develop potential field methods for navigation in dynamicenvironment. In , the velocity of dynamic obstacles is included in the definition of potential function. The basic problem is that the collision depends on the velocity of both robot and obstacle; however, , considers only the speed of the robot. In  relative positions and velocities of the mobilerobot with respect to the obstacles are considered in definition of potential function. However, this method needs exact knowledge of velocity of dynamic objects, which is not available in practice. Potential function to reach a moving target is defined in , but the velocities of the robot, obstacles and target are assumed to be known.
A* path-finding search algorithm is very famous in games for finding shortest distance between two nodes. Today’s games have thousands of agents moving at a same time in the presence of obstacles. Thus it has become very important to find shortest paths concurrently and in a speedy way. Making use of Mobilerobotsystem nature suits this scenario perfectly. Implementing Simple A* algorithm using arrays (Parallel A*) has approximately the same results as compared to A* implementation using adjacent lists. Both implementations are greedy for space. Increase in the size of map increases the memory requirements and thus decreases the speed of algorithm. To further increase the overall performance of algorithm, the memory requirements must be reduced. One option is to use the fast, read-only constant memory for storing the map. Pre-computing some paths and then sharing this already computed information with other agents further increases the efficiency. Another solution to this problem is to exploit the parallel hardware architecture in a true sense. Some improvements are made in the basic A* algorithm to calculate each path using multiple threads that run concurrently and use
MMAS was first introduced by Thomas Stutzle in the year 2000; it uses a greedier search compared to the traditional ACO algorithm . In  the performance of the algorithm has been studied on the TSP problem. Later, the research on MMAS has progressed to studying the performance when implemented on a grid map in a static environment. On the contrary, in  MMAS has been implemented on a topological map and the paths that need to be traveled by the ants are represented by a sequence of actions that the ants should execute to reach the goal and the traveled distance spent by ants was analyzed. However, the performance of the MMAS algorithm was not evaluated when implemented on a large scale environment in both grid and topological maps. The grid map represents the robotenvironment(search space) even with minor details. Therefore, I found that as a motivation to further study the performance of the MMAS algorithm on a grid map(of sizes starting from 10x10 until 400x400) in a dynamicenvironment and contribute to the field of research.
The development of mobilerobot technology shows the scientific and technological levels of a country to a cer- tain extent. Although the research of mobilerobot in China has made a lot of achievements in recent years, there are still many technical problems. Aiming at the problems of traditional pathplanning methods for mo- bile robots, Kalman filter is used to fuse data and realize localization, which makes it possible for mobile robots to use this localization method to obtain higher localization accuracy. In order to better realize the pathplanning of mobilerobot, the traditional artificial potential field method is introduced, and an improved dynamic arti- ficial potential field method is proposed to overcome the shortcomings of the traditional artificial field potential method. In order to verify the proposed algorithm, the pathplanning effect of tracked mobilerobot in static com- plex environment is tested by simulating in the laboratory of 4.5 m × 3.5 m. The simulation results show that the im- proved pathplanningalgorithm can make the mobilerobot avoid obstacles smoothly in complex and narrow space and realize the optimal pathplanning to achieve the set goal. Due to the limited experimental environment,
Pathplanning of a robot  is a problem in which a problem space or a problem domain with a number of obstacles is given and the aim is to find a collision free path which a robot can follow in order to reach its destination from the start position. Here in this paper environment is represented by ordered grids, each of which represents a location in the mobilerobot movement space. Along with this we would be able to place dynamic no. of static and movable obstacles at run time. The boundary of obstacles is formed by their actual boundary plus minimum safety distance considering the size of the mobilerobot, which makes it possible to treat the mobilerobot a point in the environment. The path  which the robot will follow is desired to be optimal in terms of distance and time taken by the robot to reach the destination. Here in this problem we have used Genetic Algorithm for pathplanning which is a search algorithm based on the mechanics of natural selection and natural genetics. Potential solutions of a problem are chromosomes, which form a population of possible solutions which compete with each other on the basis of fitness function. A selection mechanism based on the fitness is applied to the population and the individuals strive for the survival.
Remarkable work has been made in the firefly algorithm to apply it on different applications or improve on it, such works as a novel algorithm for global optimization presented by Zhang et al. when they combine the firefly algorithm with the Differential Evolution (DE) getting the benefits of both algorithms for better results  , Xin-She Yang did the same previously combining the Firefly with the Levy Flights algorithms formulating new powerful metaheuristic algorithm  . Other authors on the other hand had modified on the firefly itself to suit their work aspect, such as Wang et al. proposing a nee modified firefly algorithm to solve the UCAV pathplanning problem  , and Liu et al. who modified version of the firefly algorithm was proposed for AUVs pathplanning problem in three-dimensional form  .
Abstract: In this survey we have presented the detailed survey of pathplanning algorithms and techniques available so far. All the available methods and techniques are systematically understand and presented in the proper manner in order to make a research gap in the available techniques. The pathplanning algorithms are applied on static as well as in the dynamicenvironment. We also present the techniques based on these environments. The Approaches are classified into classical and reactive approaches. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigational algorithms are applied on static as well as in the dynamicenvironment for analysis and it has been conclude that the reactive methods are more suitable for pathplanning and navigation of mobilerobot.
Every automated and intelligent robotic system uses planning to decide the motion of root and a real world. Robotics is an extremely inter-disciplinary area that takes inputs from numerous disciples and fields with varying complexities. The autonomous robots make extensive use of sensors to guide them in and enable them to understand their environment. A mobilerobot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors . Soft Computing Tools and Techniques are finding increasing application in the field of robotics. Many of the robotic problems like planning, coordination, etc. are complex problems that are now-a-days solved using the most sophisticated soft computing tools. In many real-world applications, the workspace of robots often involves various entities that robots must evade, such as a fire in the rescue mission, objects in household applications, and instruments in automated factories .
path is safe.Most of the methods tried to expand the size of obstacles to ensure the safety of mobilerobotpathplanning, the consequences is some of good path is cut off. APF can carry out safe pathplanning, but it did not conduct a global search, thus the final path is neither the shortest nor the safest. A genetic algorithm based safe pathplanning method in a static environment is stated in literature , and a genetic algorithm based safe pathplanning method in a dynamicenvironment is stated in literature , but it is a more complicated calculation. Modeling terrain techniques have been more and more compactly integrated with robotics. Environment models are useful in mobilerobot mapping, location and navigation. But, the amount of data contained in these models is too huge to deal with. Some models are even too large to fit in main memory, and can not fulfill the requirement of real-time computing. These models would also cause inconvenience in rendering and transmitting. Therefore, building compact maps of the environment models has become an indispensable research topic.This paper presents a strategy of safe pathplanning for mobilerobot in dynamic environments based on compact maps. Knowledge-based genetic algorithm is employed to find global optimization of safety path; in the algorithm, the relationship between adapt values and gap is linear, the calculation is relatively simple at the same time. Incorporating of domain knowledge into the initial population and the adaptive adjustment of control parameters greatly improved the efficiency of genetic algorithm, a safe pathplanning in dynamic environments is achieved. The simulation results show the correctness and validity of this method.
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 this research, the ant colony optimization (ACO) algorithm is applied to find the shortest and collision free route in a grid network for robotpathplanning. Obstacles with various shapes and sizes are considered to simulate a dynamicenvironment. Computer simulation results demonstrate that the ACO algorithm can successfully re-route the optimal path for the new network after obstacles are added. Future works may include the investigation on different ACO algorithms, such as the Elitist Ant System (EAS), the Rank-Based Ant System, and the MAX-MIN Ant System (MMAS). Also, simulation works can be performed with more complicated networks and obstacles.
A locally-excitatory globally-inhibitory oscillator network (LEGION)  described by ordinary differential equations can extract image regions (connected components) from a given image and exhibit extracted image regions in a time series according to the synchronization of oscillators. We call this function “dynamic image segmentation”. A LE- GION is novel but needs a huge computational cost to perform dynamic image segmentation because of numerical integration.