Abstract A practical **path** **planning** method for a **multiple** **mobile** **robot** **system** (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 **path** **planning** methods. In this paper, we present a practical **cooperative** **path** **planning** **algorithm** for MMRS in a **dynamic** **environment**. First, each **robot** uses an analytical method to plan an obstacle-avoidance **path**. Then, a distributed prioritized scheme is introduced to realize **cooperative** **path** **planning**. 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**.

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In this paper, A* **path** **planning** **algorithm** has been represented for a **mobile** **robot** to be able to follow a constructed **path** from its current position to a specified goal within its **environment**. To ensure that the **mobile** **robot** follow the constructed **path** by **path** **planning** **algorithm**, 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.

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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 **path** **planning** for **mobile** **robot** 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.

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(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 **path** **planning** for a **mobile** **robot**. 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 [15] 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.

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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 [1]. 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 [4],[6]. The EKF SLAM approach to slam is to develop a filtering process for the **system** [7]. Our work operates on the two-dimensional SLAM in a three-dimensional **environment** [3]. 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 **dynamic** **system**.

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Z. Guesssoum and al [1] 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 [4] 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 [5]. This approach treats the case of **cooperative** systems and it obliges agents to interrupt their executions in order to evaluate their criticality.

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Aim at this problem the **dynamic** window method is used in this paper. It has a good obstacle avoidance capability in the **dynamic** **environment** [6]. But this method does not satisfy the global optimal **path** **planning**. The node-based A* **algorithm** is suitable for global **path** **planning**. It has the advantages of simple data, small calculation compared to the biological inspired GA, PSO, etc [7] [9]. But when the environmental space increases, it needs large storage space, low efficiency, and poor real-time decision-making [11]. 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 [1]. Aim at these problems the **algorithm** is improved. It makes the **path** smoother on the basis of the overall optimization of the **planning** **path** [12]. so a global **dynamic** **path** **planning** 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 **path** **planning** 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 **planning** **path**. At the same time it improves the local obstacle avoidance.

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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 **mobile** **robot** 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 **path** **planning** task is usually one of many important tasks that these robots must accomplish. Autonomous **mobile** applications require efficient and adaptive **path** **planning** 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 **path** **planning** (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.

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Potential field is one of the most common methods for **path** **planning**. The popularity of this method is due to its simplicity for implementation. This method was introduced by [5] and then improved for real time implementations in [18]. First, potential field approach was suggested for navigation in static **environment** [19], [20], [21], [22]. However, the real world is not stationary, and the **robot** moves in **dynamic** **environment** and encounters **dynamic** obstacles like moving humans. Researchers started to develop potential field methods for navigation in **dynamic** **environment**. In [23], 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, [23], considers only the speed of the **robot**. In [24] relative positions and velocities of the **mobile** **robot** 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 [25], but the velocities of the **robot**, obstacles and target are assumed to be known.

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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 **Mobile** **robot** **system** 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

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MMAS was first introduced by Thomas Stutzle in the year 2000; it uses a greedier search compared to the traditional ACO **algorithm** [24]. In [24] 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 [21] 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 **robot** **environment**(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 **dynamic** **environment** and contribute to the field of research.

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The development of **mobile** **robot** technology shows the scientific and technological levels of a country to a cer- tain extent. Although the research of **mobile** **robot** in China has made a lot of achievements in recent years, there are still many technical problems. Aiming at the problems of traditional **path** **planning** 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 **path** **planning** of **mobile** **robot**, 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 **path** **planning** effect of tracked **mobile** **robot** 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 **path** **planning** **algorithm** can make the **mobile** **robot** avoid obstacles smoothly in complex and narrow space and realize the optimal **path** **planning** to achieve the set goal. Due to the limited experimental **environment**,

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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 [8] , Xin-She Yang did the same previously combining the Firefly with the Levy Flights algorithms formulating new powerful metaheuristic **algorithm** [9] . 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 **path** **planning** problem [10] , and Liu et al. who modified version of the firefly **algorithm** was proposed for AUVs **path** **planning** problem in three-dimensional form [11] .

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Abstract: In this survey we have presented the detailed survey of **path** **planning** 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 **path** **planning** algorithms are applied on static as well as in the **dynamic** **environment**. 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 **dynamic** **environment** for analysis and it has been conclude that the reactive methods are more suitable for **path** **planning** and navigation of **mobile** **robot**.

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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 **mobile** **robot** exploring an unknown **environment** has no absolute frame of reference for its position, other than features it detects through its sensors [1]. 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 [2].

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The main contribution of this study is to provide **path** **planning** alternatives for **mobile** robots using cellular learning automata. Existing **path** **planning** methods for **mobile** robots implemented in ROS (**Robot** Operating **System**) [17] were designed using Dijkstra’s **algorithm** and the A* **algorithm**, where the **path** **planning** 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.

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In this research, the ant colony optimization (ACO) **algorithm** is applied to find the shortest and collision free route in a grid network for **robot** **path** **planning**. Obstacles with various shapes and sizes are considered to simulate a **dynamic** **environment**. 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.

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A locally-excitatory globally-inhibitory oscillator network (LEGION) [4] 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.