TIME-OPTIMAL TRAJECTORY GENERATION FOR INDUSTRIAL ROBOTS BASED ON ELITE MUTATION SPARROW SEARCH ALGORITHM

To mprove the efficency anstaty of nustraroots, a To i p ove the efficie cy a d stability of i dust ial obots a tme optmatraectory pannng metoaseon an ete mu time-optimal trajectory planning method based on an elite mu-taton sparrow searcagortm EMs proposeFrst a tation sparrow search algorithm (EMSSA) is proposed . First, a non unform septc B spne nterpoaton traectory functon s con non-uniform septic B-spline interpolation trajectory function is con-structe, wcovercomes te sortcomng of unsmootont ac-structed which overcomes the shortcoming of unsmooth joint ac ceeraton or ern ow orer nterpoaton anassgns nematc celeration or jerk in low-order interpolation and assigns kinematic parameters at the startng anstoppng ponts. econ, the fitness pa a ete s at the sta ti g a d stoppi g poi ts Seco d the fit ess functon s constructet mnmzes te sum of tme ntervas function is constructed . It minimizes the sum of time intervals etween two aacent nots n B spne traectory conserng ne between two adjacent knots in B-spline trajectory considering kine-matc constrants n EMs proposeto sceue te tme matic constraints. An EMSSA is proposed to schedule the time ntervas angenerate te tme optmaseptc B spne traectory intervals and generate the time-optimal septic B-spline trajectory. Ete reverse earnng strategy s useto optmze te ntapopua Elite reverse learning strategy is used to optimize the initial popula-to a accee ate the co ve ge ce speeof the ago th Beses tion and accelerate the convergence speed of the algorithm. Besides, to enance te souton quaty anavofang nto ocaoptmza to enhance the solution quality and avoid falling into local optimiza-ton te agortm s mprovey cosne escenng searcstep tion, the algorithm is improved by cosine -descending search step annormaaucy mutaton strateges Furtermore an exampe and normal -Cauchy mutation strategies. Furthermore, an example s gven to verfy tat te proposeagortm s eectve n sovng is given to verify that the proposed algorithm is effective in solving the te optat aecto y pa g p oe wth utco st at the ti me-opti mal trajectory planni ng problem with multi-constrai nt anas te avantages of fast sovng speegprecson anand has the advantages of fast solving speed , high precision, and gooroustness. good robustness