[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82959-en":3,"doc-seo-82959-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82959,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators","Trajectory planning strongly affects tracking accuracy, actuator demand, and execution behavior in robotic manipulators. Classical generators such as cubic, quintic, and trapezoidal profiles are popular for smoothness, but they remain kinematic and neglect manipulator dynamics and control effort during generation. The framework presented incorporates dynamics and actuator effort in a finite-horizon optimal control formulation, using midpoint linearization for improved accuracy on large motions. A unified evaluation keeps closed-loop nonlinear dynamics and constraints identical across planners, and simulations on a nonlinear simplified UR5 show reduced tracking error, corrective torque, and execution cost.","Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators  \nSachintha Bhanuka Dayawansa  \nDepartment of Electronic and Telecomm. Engineering University of Moratuwa Katubedda, Sri Lanka [dayawansasb.22@uom.lk](dayawansasb.22@uom.lk)  \nSudath Rohan Munasinghe  \nDepartment of Electronic and Telecomm. Engineering University of Moratuwa Katubedda, Sri Lanka University of Tartu, Estonia [rohan@uom.lk](rohan@uom.lk)  \narXiv :2607 .05544v2 [ cs .RO] 8 Jul 2026  \nAbstract—Trajectory planning strongly influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators. Classical planners such as cubic, quintic, and trapezoidal profiles are widely used for their simplicity and smoothness, yet they remain purely kinematic and ignore system dynamics and control effort during trajectory generation. Asa result, nominally smooth trajectories can lead to inefficient nonlinear execution and increased corrective control action. This paper presents a control-aware optimal trajectory planning framework that explicitly incorporates manipulator dynamics and actuator effort within a finite-horizon formulation. A midpoint linearization strategy is introduced to improve approximation accuracy for large point-to-point motions. In contrast to prior comparisons, the proposed approach enables fair, isolated evaluation of trajectory generation effects under identical closed-loop nonlinear execution conditions. To this end, a unified evaluation framework is developed in which all planners are executed under identical nonlinear dynamics, controller structure, and actuator constraints. Simulations on a nonlinear simplified UR5 manipulator show that the proposed approach consistently reduces tracking error, corrective torque, and closed-loop execution cost compared to classical methods, achieving substantial reductions in actuator effort and execution cost across all evaluated scenarios, demonstrating that kinematic smoothness alone does not ensure dynamically efficient execution.  \nIndex Terms—trajectory planning, optimal control, robotic manipulators, nonlinear execution  \nI. INTRODUCTION  \nTrajectory planning significantly influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators. Classical trajectory generation methods such as cubic polynomials, quintic polynomials, and trapezoidal velocity profiles are widely used due to their simplicity, computational efficiency, and smoothness properties [1] . However, these approaches are fundamentally kinematic and do not explicitly account for manipulator dynamics or actuator control effort during trajectory generation [1], [2] . Consequently, trajectories that appear smooth in joint space may still produce unfavorable acceleration distributions, elevated corrective actuator demand, and dynamic inefficiencies when executed under nonlinear manipulator dynamics [3] .  \n© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nIn practical robotic systems, trajectories are executed through feedback controllers operating on nonlinear manipulator dynamics; therefore, trajectory quality cannot be evaluated solely from kinematic smoothness. Even geometrically smooth trajectories may impose substantial corrective burden on the feedback controller, leading to aggressive actuator behavior and increased execution cost. This raises an important question regarding whether widely used classical trajectory planners remain dynamically efficient under realistic nonlinear execution conditions [4], [5] .  \nOptimal control methods provide a framework for generating dynamically feasible trajectories","cbCailrOMWetNcm5","https://ap.wps.com/l/cbCailrOMWetNcm5","pdf",2954453,3,1,7,"English","en",105,"# Abstract\n# Introduction\n## Motivation: Limitations of classical planners\n## Optimal control and actuator-aware trajectory planning\n## Unified evaluation framework and contributions","[{\"question\":\"Why can classical cubic, quintic, and trapezoidal trajectory planners be inefficient in robotic manipulation?\",\"answer\":\"They are fundamentally kinematic and do not explicitly account for manipulator dynamics or actuator/control effort during trajectory generation. As a result, trajectories that look smooth in joint space can still cause unfavorable accelerations and higher corrective actuator demand during execution.\"},{\"question\":\"What does the control-aware optimal trajectory planning framework optimize?\",\"answer\":\"It uses a finite-horizon optimal control formulation that penalizes both state deviation and actuator effort. A midpoint linearization strategy improves approximation accuracy for large point-to-point motions.\"},{\"question\":\"How is a fair comparison between planners ensured in the paper?\",\"answer\":\"All planners, including classical methods and the proposed optimal planner, are executed under identical nonlinear manipulator dynamics, the same PID tracking controller structure and gains, and the same actuator constraints. This isolates differences caused by trajectory generation itself.\"}]",1784184335,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"dynamic-evaluation-of-classical-and-control-aware-optimal-trajectory-planning-in-robot-manipulators","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/dynamic-evaluation-of-classical-and-control-aware-optimal-trajectory-planning-in-robot-manipulators/82959/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can classical cubic, quintic, and trapezoidal trajectory planners be inefficient in robotic manipulation?","Question",{"text":75,"@type":76},"They are fundamentally kinematic and do not explicitly account for manipulator dynamics or actuator/control effort during trajectory generation. As a result, trajectories that look smooth in joint space can still cause unfavorable accelerations and higher corrective actuator demand during execution.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the control-aware optimal trajectory planning framework optimize?",{"text":80,"@type":76},"It uses a finite-horizon optimal control formulation that penalizes both state deviation and actuator effort. A midpoint linearization strategy improves approximation accuracy for large point-to-point motions.",{"name":82,"@type":73,"acceptedAnswer":83},"How is a fair comparison between planners ensured in the paper?",{"text":84,"@type":76},"All planners, including classical methods and the proposed optimal planner, are executed under identical nonlinear manipulator dynamics, the same PID tracking controller structure and gains, and the same actuator constraints. 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