摘要
在移动机器人路径规划问题的研究中,路径规划的实质是机器人按照一定的技术指标找到一条从起点到终点与障碍物无碰撞的最短路径。由于路径长度和安全性指标是相互矛盾的两个技术指标,大多数现存算法在把它们作为单目标优化时容易陷入局部最优,用多目标优化中的帕累托最优则能够很好地平衡和解决这两个目标不同资源分配下的组合情况。用贝塞尔曲线来描述路径,用帕累托最优解决路径长度和安全性指标之间的共存问题。实验结果表明,比起单目标处理,多目标优化在解决路径优化问题中路径长度及安全性指标时较稳定,能够找到满足条件的更短路径,并且帕累托最优能够很好地解决不兼容目标之间的共存问题,找到不同安全性指标下优化最短路径。
The essence of robotic path planning is to find a collision free path from the start location to the target location in an environment with obstacles which satisfy certain optimum criteria. Criteria of path length and security are contradictory issues in path planning problem, most existing algorithms regard it as a single objective problem and are easily to be trapped into local optima while Pareto optimality in multi objective optimizer can balance and solve the combination well under different resource assigned of two goals. In the paper, Bezier curve was used to describe path in this task, and Pareto optimality was utilized to solve the coexistence of path length and security. The result shows that compared with single objective optimizer, the multi objective optimizer is more stable in achieving the criteria of path length and security and can find the shortest path which satisfies certain conditions. The Pareto opti mality can effectively solve the coexistence problem of incompatible objectives and find the shortest path under differ ent safety criteria.
出处
《计算机仿真》
CSCD
北大核心
2014年第4期364-368,共5页
Computer Simulation
基金
国家自然科学基金项目(60905039)
中国博士后科学基金特别资助项目(2012T50639)
教育部高等学校博士学科点专项科研基金(20114101110005)
河南省科技攻关项目(132102210521)
关键词
贝塞尔曲线
多目标优化
帕累托最优
Bezier curve
Muhi -objective optimizer
Pareto optimality
