Trajectory planning of an autonomous mobile robot

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2019-12-06 DOI:10.1504/ijsi.2019.10025726
S. Pattanayak, B. B. Choudhury
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Abstract

The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimisation technique called as adaptive particle swarm optimisation (APSO) and particle swarm optimisation (PSO) for trajectory length optimisation. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.
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自主移动机器人的轨迹规划
自主移动机器人轨迹规划的最新进展是针对一项流行的调查工作。本文介绍了改进的粒子群优化技术,即自适应粒子群优化技术(APSO)和弹道长度优化的粒子群优化技术(PSO)。为了估计机器人的轨迹长度,在静态环境中,在起始点和目标点之间选择9个障碍物。最后对这两种方法进行了比较,找出了以最少的计算时间和最短的行程时间提供最短的轨迹长度的方法。仿真结果表明,与粒子群相比,粒子群能以更少的计算量和运行时间缩短弹道长度。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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