外文翻译--基于Dubins路径的智能车辆路径规划算法研究 英文版

1> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment. For example, “This work was supported in part by the U.S. Department of Commerce under Grant BS123456”. The next few paragraphs should contain the authors current affiliations, including current address and e-mail. For example, F. A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305 USA (e-mail: author boulder.nist.gov). S. B. Author, Jr., was with Rice University, Houston, TX 77005 USA. He is now with the Department of Physics, Colorado State University, Fort Collins, CO 80523 USA (e-mail: authorlamar.colostate.edu).T. C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA, on leave from the National Research Institute for Metals, Tsukuba, Japan (e-mail: authornrim.go.jp).Song Guo-Hao,Huang Jin-Ying,Lan Yan-Ting(School of Mechanical and Power Engineering, North University of China Taiyuan,030051)AbstractThe path planning is one of the core issues of intelligent vehicles. All paths can be decomposed into Dubins path. This paper did sectional research into the intelligent vehicles travel path under the idea of Dubins path and carried out tests on the execution performance of the algorithm using PID control strategy. Researches showed that this algorithm could calculate the vehicles shortest path, reduced the vehicles path length, shortened the driving time, reduced the computation amount of the control system, improved the enforcement of the vehicle execution system, reduced the execution error, had a good selectivity of the optimal path.Index TermsIntelligent Vehicles, path planning,Dubins path, the shortest path. I. INTRODUCTIONPATH planning is used in many fields, such as: military unmanned aircraft, space exploration robot, intelligent vehicle, surveillance and reconnaissance and so on1-3. Path planning is a hot area of research in the field of modern vehicle, which needs to consider many factors, such as: constraints from vehicle itself, constraints of driving environment and other issues. In the planning of driving route, we should plan out of the scope of vehicle as far as possible under the premise of safe driving and make the vehicle bypass obstacles autonomously. Path planning algorithm should have precision, occupy less memory, meet the requirements of real-time, and have no obvious delay problems during the implementation4-5. In addition, in order to make the driving path optimal and improve the driving efficiency, it is necessary to shorten the driving length of the vehicle.There are many related research of path planning. Such as the Tentacle Algorithm proposed by Zhang Minghuan, et al6. This algorithm planned the route that the vehicle will driving at first, let the vehicle driving according to the planned 16 * 81 usable routes. In this way, the vehicle could save a lot of reaction time, but it was not able to handle mutation, and the research background was too idealistic. Wang Kai, et. al7. proposed the improved-artificial potential field method. This algorithm could be applied to obstacle avoidance stages in intelligent car path planning, solved the problem about the vehicle easily trapped in local minimum in the traditional artificial potential field method in path planning, had certain real-time performance. But it is limited by the influence of the sensor performance and its function scope is small, and it is easily affected by the external environment. Jiaojie Li et al8. proposed the coordinated obstacle avoidance algorithm. This algorithm applied the first-order kinetics and second order kinetics in the process of driving to conduct no-speed monitoring and bypass the obstacles. But it handled all obstacles as static treatment, and do not have flexibility. It may happens that two cars avoid obstacles at the same time but nowhere to avoid.This paper did research into the intelligent vehicle travel path under the idea of Dubins path. This algorithm can well decide out of the optimal path when driving on the road and can solve the problem of the obstacle avoidance between many obstacles. This algorithm has good real-time performance and small delay. II. The Choice of PathsThe main purpose of path planning is to seek a safe and fast driving route, and make the vehicle driving to the end. Generally speaking, vehicles driving in the area of the known or partially known areas, which means areas with some static obstacles. Now, we use P(x,y,v,a) as driving states,(x,y) as driving position. Parameters in (,v,a) respectively represent driving deviation angle, speed and acceleration. If we put the path of the vehicles driven from the starting point P0 to the ending