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Finding Proper Configurations for Modular Robots by Using Genetic Algorithm on Different Terrains
| Content Provider | Semantic Scholar |
|---|---|
| Author | Klidbary, Sajad Haghzad Shouraki, Saeed Bagheri Faraji, Salman |
| Abstract | planning methods, the environment is limited to two dimensions and obstacles are presented by polygon shapes [4]-[8]. So far, many methods have been introduced to describe the environment such as visibility graph [9], Voronoi diagram [10], MAKLINK graph [11] and cell decomposition [12]. Various search algorithms have been used such as artificial potential field method [13], neural networks [14], ant colony algorithm [15], particle swarm optimization [16] and genetic algorithm [2]-[6], [8]. Each method has its own advantages over others in certain aspects. In the recent years, genetic algorithms have been widely used in the field of path planning for mobile robots. So far, most of presented algorithms are based on fixed-structure and they have not addressed path planning and online reconfiguring, simultaneously [16]. So they are not suitable path planning methods for modular robots. In this paper, according to the capability of new designed modular robot to change configurations, the GA is presented to produce a proper path and configuration pattern for crossing the environment. Path evaluation criteria are combined with minimum time, lowest energy and shortest distance. Chromosomes are consisting of different paths and different configurations with variable length. In our method, unlike most of earlier methods, all chromosomes in initial population and after applying GA operators are feasible without having collision with obstacles. Simulation results prove that our method can successfully plan a path and configuration pattern for modular robots with convincing performance, compared to fixed-structure robots. The rest of the paper is organized as follows: in Section II, our new module design is explained in details together with its local navigation method. The proposed GA is introduced in Section III. In Section IV, Dijkstra algorithm is used for modular robot path planning. In Section V simulation results of GA and Dijkstra algorithm in various environments are presented and analyzed. Finally, the conclusion and suggestions for future research are given in Section VI. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijmmm.org/papers/078-A011.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Ant colony optimization algorithms Artificial neural network Chromosomes Computation Dijkstra's algorithm Fitness function Gallium Genetic algorithm Graph - visual representation MATLAB Mathematical optimization Mobile robot Motion planning Neural Network Simulation Particle swarm optimization Population Randomness Robot (device) Search algorithm Self-reconfiguring modular robot Short Software release life cycle Time complexity Visibility graph Voronoi diagram collision |
| Content Type | Text |
| Resource Type | Article |
