Abstract
SUMMARYIn this work, an optimal maneuverability strategy for car-like unmanned vehicles operating in restricted environments is presented. The maneuverability strategy is based on a path planning algorithm that uses the environment information to plan a safe, feasible and optimum path for the unmanned mobile robot. The environment information is obtained by means of a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm uses the sensors' information to build a map of the surrounding environment. A Monte Carlo sampling technique is used to find an optimal and safe path within the environment based on the SLAM information. The objective of the planning is to safely reach a desired orientation in a bounded space. Theoretical demonstrations and real-time experimental results (in indoor and outdoor environments) are also presented in this work.
Publisher
Cambridge University Press (CUP)
Subject
Computer Science Applications,General Mathematics,Software,Control and Systems Engineering
Reference43 articles.
1. Closing Loops With a Virtual Sensor Based on Monocular SLAM
2. J. K. Pothal , D. R. Parhi and M. K. Singh , “Navigation of Multiple Mobile Robots Using Swarm Intelligence,” World Congress on Nature Biologically Inspired Computing (2009).
3. Towards features updating selection based on the covariance matrix of the SLAM system state
4. Y.-H. Qu , Q. Pan and J.-G. Yan , “Flight Path Planning of UAV Based on Heuristically Search and Genetic Algorithms,” In: 31st Annual Conference of IEEE on Industrial Electronics Society (IECON 2005) (Nov. 2005) pp. 5.
5. Pattern Recognition
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献