摘要
对挖掘机进行时间最优轨迹规划时,通常采用分段插值策略。但是,常用的3-3-3-3-3和3-3-5-3-3分段插值策略存在插值结果不精确、关节存在往复运动、挖掘轨迹不平滑以及工作过程中挖掘机各关节所受冲击较大等不足。为了避免上述问题,提出了基于4-3-3-3-4分段多项式的轨迹规划策略,以各关节角速度和角加速度为约束条件,分别将粒子群算法和差分进化算法用于基于4-3-3-3-4分段插值策略的时间最优轨迹优化,并将优化结果与常用的3-3-3-3-3和3-3-5-3-3分段插值策略进行了对比。实验结果表明:4-3-3-3-4分段插值策略通过粒子群算法优化得到的各关节的运动轨迹更平滑,可以有效地降低各关节在工作过程中受到的冲击,而且挖掘机完成规划轨迹所需时间更短,从而大大地提高挖掘机的工作效率;同时,粒子群算法在求取时间最优值方面所用时间短,显著提高了轨迹规划的实时性。
The piecewise interpolation strategy is usually adopted to realize time optimal trajectory planning for an excavator.But the commonly used 3-3-3-3-3 and 3-3-5-3-3 piecewise interpolation strategies have some problems such as inaccurate interpolation results,reciprocating motion of the joints,unsmooth motion trajectory,and large shock produced during the work of the excavator.In order to avoid those problems,a trajectory planning strategy based on 4-3-3-3-4 piecewise polynomial is proposed in this paper.The particle swarm optimization(PSO)and differential evolution(DE)algorithm are respectively carried out complete the time?optimal trajectory optimization based on 4?3?3-3-4 piecewise interpolation strategy under the constraints of joint angular velocity and angular acceleration,and the optimization results are compared with commonly used 3-3-3-3-3 and 3-3?5?3?3 interpolation strategy.The experimental results show that the motion trajectories of each joint obtained by the 4-3-3-3-4 piecewise interpolation strategy is smoother,which can effectively reduce the shock of each joint produced during the work.Moreover,the time required for the excavator to realize the planning trajectory is shorter,thereby greatly improving the working efficiency of the excavator.In addition,the time required to complete the time optimal trajectory planning by the particle swarm optimization algorithm is shorter,then the real-time performance of the trajectory planning is greatly improved.
作者
李虹
张韵悦
孙志毅
孙前来
LI Hong;ZHANG Yunyue;SUN Zhiyi;SUN Qianlai(School of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024.Shanxi,China)
出处
《中国工程机械学报》
北大核心
2020年第1期7-13,共7页
Chinese Journal of Construction Machinery
基金
山西省青年科技基金资助项目(2015021089)
山西省重点研发计划资助项目(201703D121028-1)。
关键词
挖掘机
分段多项式
粒子群优化算法
差分进化算法
轨迹规划
excavator
piecewise polynomial
particle swarm optimization(PSO)
differential evolution(DE)algorithm
trajectory planning
