多机器人搜索中N-GCPSO算法与空间粒子扩展算法的集成

Hafidlotul F. Ahmad, M. Hardhienata, K. Priandana
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引用次数: 4

摘要

本文研究了多机器人搜索问题,其中一组机器人必须发现自己并将自己分配给目标。为了解决这个问题,我们在机器人中嵌入了一种称为邻域保证收敛粒子群优化(N-GCPSO)的算法。本研究在模拟环境中考虑了这个问题。为了减少机器人之间的碰撞,我们将N-GCPSO算法与空间粒子扩展算法相结合。仿真结果表明,将N-GCPSO与空间部分扩展算法相结合,在不降低N-GCPSO发现和分配目标性能的前提下,减少了机器人之间的碰撞次数,提高了N-GCPSO的有效性。
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Integration of N-GCPSO Algorithm with Spatial Particle Extension Algorithm for Multi-Robot Search
This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.
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