To solve the multi-objective **mobile** **robot** **path** **planning** in a dangerous environment with dynamic obstacles, this paper proposes a **modified** **membrane**-**inspired** **algorithm** **based** on **particle** **swarm** **optimization** (mMPSO), which combines **membrane** systems with **particle** **swarm** **optimization**. In mMPSO, a dynamic double one-level **membrane** structure is intro- duced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair **algorithm** is presented to change an infeasible **path** into a feasible **path**; a smoothness **algorithm** is proposed to remove the redundant information of a feasible **path**; **inspired** by the idea of tightening the fishing line, a moving direction adjustment for each node of a **path** is introduced to enhance the **algorithm** performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO. Keywords: **Membrane** computing, evolutionary **membrane** computing, **particle** **swarm** **optimization**, variable dimensions, **mobile** **robot** **path** **planning**, **membrane** systems.

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It should be a **robot** that is able to perform the designated tasks by itself without human intervention. It is scientifically known as artificial intelligent **robot** as it is able to ‘think’ before making decision and ‘act’ accordingly then. This research focuses on the autonomous **mobile** **robot** that is able to move into an unknown environment. The **robot** must ‘think’ how it should move. According to Pirahansiah et al. (2013), the challenges faced by autonomous **robot** are the environment factors, its capability to explore, navigate without any knowledge on the unknown environment and generates its own map for the environment. Another challenge faced by the **robot** is its capabilities to recognize its own position, landmark and any obstacles, and making decision **based** on the new environment data and is able to navigate through the environment without human intervention.

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The global optimal **path** **planning** as the second factor for **mobile** robots have been a hotspot research area for many years, and several **optimization** methods such as potential field method [1-3], visibility graph method [2] , grid method [3-5], **modified** simulated annealing **algorithm**[9] and straight line **path** **planning**[10] have been developed to solve this problem. For the grid method, the main problem is how to determine the size of grid, which has great influence on both the representation precision for obstacles and the planned **path**. In recent years, many intelligent algorithms were applied to the **path** **planning** for **mobile** robots, such as fuzzy logic and reinforcement learning [6], neural network [7], genetic **algorithm** [8], and so on.

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best solution and that solution called pbest. **Particle** **swarm** optimizer is also keeping track another value called the best value that get by **particle** in the neighbors of the **particle**. This location is called lbest. When one of particles takes all the population as its topological neighbors, the best value is a global best and it is named gbest [16]. The PSO **algorithm** is used to find the optimal parameters for the two PID controllers, one for controlling velocity and another for controlling angle of **mobile** **robot**. Figure (4) shows the block diagram of PID-PSO controller for the **mobile** **robot**.

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The probabilistic roadmap method is also a popular **path** **planning** scheme for **mobile** robots. This method uses a sampling technique to discover a sparse representation of obstacles in a configuration space. Reference [9] makes use of a probabilistic roadmap to avoid occlusions of the target and any obstacles. The probabilistic roadmap method is easily implemented and can be applied to complex environments. However, this method may fail when the environment does not have a sufficient number of free points with which to construct a probabilistic map. There are also many heuristic methods that can be used for **path** **planning**, such as rapidly exploring random trees [10], neural networks [11], genetic algorithms [12], simulated annealing [13], ant colony **optimization** [14], **particle** **swarm** optimizer and fuzzy logic [15, 16]. These methods can yield feasible schemes; however, their optimality cannot be assured with any of the above-mentioned methods.

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Global **path** **planning** starts from 1980 till these days and many algorithms have been proposed by researchers. Research leads to improve global **planning** year by year. At the beginning, **path** **planning** was focusing on finding **path** to goal only. Then it became bigger issue by not only reaching the goal but also considering **optimization** criteria [3]. There are a lot of algorithms used to solve **path** **planning** problems such as heuristic methods, meta-heuristic, and rando- mized method. A*, D*, and Dikstra can be considered as heuristic methods. A* **algorithm** is considered as a greedy, graph, and heuristic search **algorithm** that is able to find sub optimal but, not optimal **path** [4]. D* **algorithm** can be consi- dered as dynamic A* which is able to reform the **path** according to new informa- tion coming from sensors [5]. Meta-heuristic methods include **particle** **swarm** **optimization** (PSO), and ant colony **optimization** (ACO) [6]. Also there are in- telligent methods, like artificial potential field (APF), genetic **algorithm** (GA). APF is used in different types of robots where the field of forces is applied on **robot**. These forces are attractive force to goal and repulsive force from obstacles [7]. Randomized methods can be divided into two categories: rapidly exploring random tree (RRT) which is more suitable for dynamic environment and proba- bilistic roadmap (PRM) for static environment.

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The actual working environment of a **mobile** **robot** is a realistic physical space, and the space handled by the **path** **planning** **algorithm** is an abstract space of the environment. Environment modeling is a very important link of the **robot** **path** **planning**. Through a large number of literature research [4], this paper uses the grid method to establish the model. The working environment is divided with grids of the same size and the obstacle model is constructed according to the working environment of the security **robot**.

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At present, the common global **path** **planning** methods are Artificial Potential Field [2], Genetic **Algorithm** [3], Ant Colony **Optimization** [4], **Particle** **Swarm** **Optimization** [5- 6], etc. **Particle** **Swarm** **Optimization** (hereinafter referred to as PSO) has fast convergence speed, less parameters needed to be set, simple implementation and so on. Therefore, it was widely used in **path** **planning**. Qin Yuanqing et al. [7] firstly applied PSO to **robot** **path** **planning** problem. They adopted Dijkstra **algorithm** to search the shortest **path** in the MAKLINK graph, and then used PSO to conduct secondary **path** **optimization**, but the ability of global **optimization** of PSO is limited by secondary **optimization**. Nie Zhibin et al. [8] proposed non-linearized inertia weight to adjust the exploration and exploitation of PSO and combining PSO with simulated annealing **algorithm** to solve the problem of easily being trapped into local optimal solution. However, it didn’t explain how to choose parameters. Li Qing et al. [9] put forward the concept of "movable area", and proposed the obstacle avoidance strategy of setting the invalid particles in the process of iteration as global optimal solution. Through the adaptive adjustment of PSO parameters, good **path** **planning** results were obtained. However, if the neighboring **particle** of the invalid particles were suboptimal, the **algorithm** can easily get into local optimal. From the above analysis, the present study is mainly aimed at **path** search **algorithm**, and the study of **algorithm** has been basically matured. However, the research of obstacle avoidance strategy is less and the obstacle avoidance strategy will make the **algorithm** fall into local optimum.

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(Das, Behera & Panigrahy, 2015) employed hybridization of improved **particle** **swarm** **optimization** (IPSO) in combination with an improved gravitational search **algorithm** (IGSA) for multi-**robot** to determine the optimal trajectory of the determined **path** in a clutter environment. IPSO possesses social characteristics which the proposed approach incorporates into the movement of IGSA. The developed hybridization IPSO-IGSA maintains the suitable equilibrium between searching and overuse due to the adoption of co-evolutionary techniques for enhancing the expedition of IGSA and **particle** positions combined with IPSO velocity together. The **algorithm** diminishes the maximum **path** length. It also reduces the time of arrival of each **robot** to its distinct destination in the environment. The **robot** generates individualistic decisions by implementing the proposed hybrid IPSO–IGSA to understand, and communicate with each other to identify the next positions from their current

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Among those **algorithm**, PSO [6,16,19] as a global evolutionary **algorithm** is **inspired** by the behaviors of stochastic **swarm** such as flocks of birds and schools of fish, which mainly uses the **swarm** intelligent to achieve the goal of **optimization**. PSO has been applied to many domains with good performance such as system identification, neural networks and system control because of its characteristics of **swarm** intelligence, intrin- sic parallelism, and inexpensive computational. However, Due to a few adjustable pa- rameters such as population size, inertia weight and acceleration coefficients, PSO suf- fered from the premature convergence and trapping in local optimum problem, and even lack of population diversity. Several improved PSO algorithms, such as PSO- linearly inertia weight, PSO-fuzzy inertia weight and PSO-nonlinear inertia weight, had been proposed [8,19]. At present those methods have obtained with a certain perfor- mance improvement, but convergence and scarce exploration was also the frequent problems in the application process.

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Abstract - This paper presents optimizing the routing process in MANET. The proposed work is about the **mobile** networks and **based** on DSDV protocol. Malicious node is detected by PSO technique. PSO is used for shortest **path** problem **inspired** by behavior of **swarm** of fishes or flocks of birds to find a good food place. In this paper, we have compared three parameters distance, delay and energy of the existing work and proposed work **based** on PSO technique that is an improved safe routing approach to transfer data from congestion free and attack safe **path**. This technique provides effectiveness in terms of energy and the time as well as provides a reliable route over the network.

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3.3 Binocular vision-**based** localization In this paper, a **modified** model of stereo vision is presented to develop a more accurate **algorithm** for computing three-dimensional information from a stereo pair of images by modification of the stereo vision model, as shown in Fig. 4 [26]. Two cameras are separated by a distance in the -direction and both optical axes are parallel. For convenience, the coordinate system centered between two cameras is called the world coordinate system. The goal is to find the coordinates , , of the world point having corresponding points , and , in left and right images, respectively. From the similar triangles of imaging as shown in Figs. 4(a) and 4(b), we have

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The distance of initial node is mainly consideration of the connection between two points. If the connection between two points does not intersect any obstacle, or not in the obstacle, it can be indicated as a direct straight-line link between two points and the initial distance is the Euclidean distance; In contrast, if the two points can not be directly linked, the initial distance can be assumed infinite. For the two types of adjacency relationship problems solving, an initial distance matrix is established **based** on the same obstacle’s polygon vertices adjacency relationship and the different obstacles’ polygon vertices adjacency relationship [11]. However, the problem of the two types of adjacency relationships can be solved by judging all the vertex-linkage line of V intersecting with the obstacle polygon, with V being a set of all vertices of the obstacle polygons, together with the initial and final points, i.e., V { v 1 , v 2 , , v i , , v n } . In addition, for the purpose of computing of the distance between vertices, an initial node distance matrix is established as E R n n , with its

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[10]. X Zhao et al. studied the irregular storage problem of automated pharmacy, and put forward to a multi-objective reservoir allocation model to improve the storage efficiency and space utilization, and presented a two-level genetic **algorithm** to solve the above problem [11]. All the above researches laid theoretical foundations of our projects, however, the above methods with the traditional models could not describe the rationalized allocation of medicine storage in warehousing system better, and moreover, though the genetic **algorithm** achieved fruitful results of solving combinatorial **optimization** problems, due to the slow convergence speed and being easy to fall into local optimum of the genetic **algorithm**, it was urgent to explore new ways to solve **optimization** problems of storage spaces in automated warehouse system.

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Similar to all stochastic algorithms, the performance of PSO+ partially depends on the initial solutions. The convergence of the **algorithm** can be significantly improved if at least one of the agents has an initial solution near the optimal solution. In this work we consider two strategies. The first strategy is to simply generate a set of random consensus. The second strategy is to randomly choose a subsequence from the input sequence as an initial solution. Although the first strategy would allow the maximum coverage, the probability that any randomly generated consensus is near the optimal solution is very low, as there are 4 l possible solutions for a motif of length l. For the second strategy, we assume that the actual binding site is closer to the consensus than to random sequences. Since there is usually about one binding site per sequence, it is very likely that some agents may select a binding site as an initial solution. More pre- cisely, assuming that the average sequence length is L and the motif length is l, the probability that a randomly selected sequence is a binding site is 1/(L − l + 1). Also because of the Check Shift step in the **algorithm**, a random solution that contains a large suffix or prefix of the binding site can often lead to the recovery of the real motif quickly. We usually allow a binding site to be shifted by two bases to its left and right, respectively. Therefore, each true binding site can provide up to 5 initial solutions that are similar to the real motif. For this reason, we suggest the minimum number of agents to be (L − l + 1)/5 to ensure a high convergence probability. In our experiments on both synthetic and real sequences, we have found that the second strategy usually leads to much faster convergence and therefore is implemented as the default option.

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In this paper, a MPSO **based** framework is used for the perfect skill of a CMOS intensifier. The cream kind of the atom **swarm** improvement figuring and weighted system, is proposed to streamline the structure factors, for instance, MOS transistor size, power and meet the given detail. The structure central inspirations driving the CMOS circuits are considered as the cost most distant extents of the **Modified** **Particle** **swarm** movement figuring. The starting outcomes show that the proposed framework possibly meets the circuit structure subtleties what's more inspiration driving restriction the chip check. Regardless of the redirection **based** structure, the beat virtuoso expansions were done to engage the CMOS to circuit central center interests. It has been demonstrated that the structure of the CMOS circuit using the MPSO approach is advantageous separated and other procedure systems. The proposed system structure can reinforce the CMOS circuit shows up. Beginning now and into the not too expelled the MPSO estimation is a competent point of view for complex key IC structure.

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The mDPSO **algorithm** proposed employs the destruction and construction procedure of the iterated greedy **algorithm** (IG) in its mutation phase. Its performance is enhanced by employing a population initialization scheme **based** on an NEH constructive heuristic for which some speed-up methods previously developed by authors are used for greedy node insertions. Furthermore, the mDPSO **algorithm** is hybridized with local search heuristics to achieve further improvements in the solution quality. To evaluate its performance, the mDPSO **algorithm** is tested on a set of benchmark instances with symmetric Euclidean distances ranging from 51 (11) to 1084 (217) nodes (clusters) from the literature. Furthermore, the mDPSO **algorithm** was able to find optimal solutions for a large percentage of problem instances from a set of test problems in the literature. It was also able to further improve 4 out of 9 larger instances from the literature. Both solution quality and computation times are competitive to or even better than the best performing algorithms from the literature.

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At present, Kalman filter is the most widely used multi-sensor information fusion technology which is the general use of distributed or federated filter structure. How- ever, the classical Kalman filter **algorithm** used by the data processing of the federat- ed filtering subsystem does not achieve the desired effect when dealing with non- Gaussian, nonlinear states and parameter estimates, since the Kalman filter is used to obtain the optimal estimation result. 1) The statistical characteristics of the external jamming noise are subject to the Gaussian distribution. 2) The statistical characteris- tics of the external jamming noise are subject to the Gaussian distribution [6, 7]. Al- most all systems in the real world have non-linear, non-Gaussian features, it is diffi- cult to meet the classic Kalman filter use conditions. Extended Kalman filter is usual- ly used when the state equation or the measurement equation is nonlinear. EKF ap- plies the Taylor expansion of the nonlinear function to the first order linearization, ignoring the other higher order terms, so that the nonlinear problem is transformed into linear, and the Kalman linear filtering **algorithm** can be applied to the nonlinear system. This solves the problem of system nonlinearity. EKF has been widely used by non-linear state estimation systems and has been widely used by people. However, this method also brings two shortcomings [8-10]. One is that when strong nonlineari- ty, EKF violates local linear assumption, Taylor expansion EKF **algorithm** may make the filter divergence. In addition, because EKF linearization process requires Jacobi- an matrix, its cumbersome calculation process leads to the realization of the method which is relative to the EKF **algorithm**. Therefore, EKF is the suboptimal filter under the minimum variance criterion.

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