There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

National Institute of Standards and Technology における超伝導研究及び生活

In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Mobile robot path planning using membrane evolutionary artificial potential field

Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning

This paper proposed a new methodology to determine the optimal trajectory of the path for multi-robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved gravitational search algorithm (IGSA). The proposed approach embedded the social essence of IPSO with motion mechanism of IGSA. The proposed hybridization IPSO–IGSA maintain the efficient balance between exploration and exploitation because of adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocity simultaneously. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all robots to their respective destination in the environment. The robot on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO–IGSA. Finally the analytical and experimental results of the multi-robot path planning were compared to those obtained by IPSO–IGSA, IPSO, IGSA in a similar environment. The Simulation and the Khepera environment result show outperforms of IPSO–IGSA as compared with IPSO and IGSA with respect to optimize the path length from predefine initial position to designation position ,energy optimization in the terms of number of turn and arrival time.

A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning

Finding a path for a robot which is near to natural looking paths is a challenging problem in motion planning. This paper suggests two single and multi-objective optimization models focusing on length and clearance of the path in discrete space. Considering the complexity of the models and potency of evolutionary algorithms we apply a genetic algorithm with NSGA-II framework for solving the problems addressed in the models. The proposed algorithm uses an innovative family of path refiner operators, in addition to the standard genetic operators. The new operators intensify explorative power of the algorithm in finding Pareto-optimal fronts in the complicated path planning problems such as narrow passages and clutter spaces. Finally, we compare efficiency of the refiner operators and the algorithm with PSO and A* algorithms in several path planning problems.

Multi-objective path planning in discrete space

In this paper we discuss on multi-objective robot path planning in continuous space with static obstacles. We focus on the point-to-point paths with three goals minimizing path length, maximizing clearance and maximizing smoothness. We propose an efficient evolutionary algorithm to solve this problem. Formulating the problem of path planning as multi-objective models.Considering energy consumption and safety of path.Proposing geometric-based operators customized for the path planning.Proposing an efficient multi-objective evolutionary algorithm.Comparing the algorithm with the other robust algorithms in difficult search problems. One of the challenging problems in motion planning is finding an efficient path for a robot in different aspects such as length, clearance and smoothness. We formulate this problem as two multi-objective path planning models with the focus on robot's energy consumption and path's safety. These models address two five- and three-objectives optimization problems. We propose an evolutionary algorithm for solving the problems. For efficient searching and achieving Pareto-optimal regions, in addition to the standard genetic operators, a family of path refiner operators is introduced. The new operators play a local search role and intensify power of the algorithm in both explorative and exploitative terms. Finally, we verify the models and compare efficiency of the algorithm and the refiner operators by other multi-objective algorithms such as strength Pareto evolutionary algorithm 2 and multi-objective particle swarm optimization on several complicated path planning test problems.

Clear and smooth path planning

In this paper, a new method is proposed for the path planning of multi-robots in unknown environments. The method is inspired by multi-objective particle swarm optimization (MOPSO) and is named multi-robot MOPSO. It considers shortness, safety, and smoothness. Due to the obscurity of the environment, the robots should decide the moving direction based on the information gathered by sensors only such that the optimal path between the start and goal positions can be found at the end of the algorithm. Sharing knowledge among the navigating robots is necessary to achieve this aim. So, a new concept, named the probabilistic window, is introduced in this paper. It combines the current information obtained through the robot sensors and experiences of the previous robots to select the paths that seem more likely to achieve higher fitness in the mentioned objectives. The proposed method has an outstanding performance on different complex benchmarks, and the results have shown that it is more effective and efficient compared with the classic and the state-of-the-art methods.

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Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

In this paper, an Image Encryption System by special kind of cellular automata (cellular automata with memory) and also an appropriate transition function for the cryptosystem have been proposed. Also a lossy idea is used to present a secure cryptosystem. The use of lossy method provides a secure method and it is shown that the result is resistant to cryptanalysis attacks, especially known plaintext and chosen plaintext. When the original image is compared with the decrypted image by human visual system, it is not recognizable which one is decrypted and which one is the original image.

Ali Mohades

Papers

Multi-objective path planning in discrete space

Clear and smooth path planning

Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

An Image Encryption System by Cellular Automata with Memory

-Concave hull, a generalization of convex hull