{"id":3830,"date":"2026-04-25T22:58:06","date_gmt":"2026-04-25T14:58:06","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=3830"},"modified":"2026-04-26T20:03:58","modified_gmt":"2026-04-26T12:03:58","slug":"jase-202609-32-012","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-012","title":{"rendered":"A Composite Objective-Driven Framework for High Performance Robotic Trajectory Optimization"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Qi Yang, Dongwei Li, Xin Zhang<\/i><\/a>, Xinyu Cai, and Shengbing Sun<\/p>\n\n\n\n

School of Mechanical Engineering, Shenyang Ligong University, Shenyang, Liaoning,China<\/p>\n<\/div>\n\n\n\n

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Received: March 2, 2026
Accepted: March 18, 2026
Publication Date: April 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall Performance Radar Chart <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.012<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In high-precision robotic tasks, achieving optimal trajectory smoothness and dynamic performance is a critical challenge. Traditional integer-order optimization methods often fail to accurately capture intrinsic nonlinear dynamics and long-term memory effects, creating a performance bottleneck. To overcome these limitations, this paper proposes a novel framework: Composite Fractional Objective for Trajectory Optimization (CFOTO). By integrating fractional calculus theory, we construct a composite objective function that incorporates both a fractional norm and a fractional-order smoothness term, which is solved using the fmincon solver. Extensive comparative simulations benchmark CFOTO against three classical and state-of-the-art planning methods. The results demonstrate that CFOTO achieves superior overall performance, exhibiting distinct advantages in trajectory smoothness and algorithmic convergence speed. This research provides a high-performance solution for robotic trajectory planning and offers practical evidence for applying fractional calculus in intelligent control.<\/p>\n\n\n\n

Keywords: Trajectory Planning, Fractional Calculus, Nonlinear Optimization, Robotic Manipulator, Smoothness<\/em><\/p>\n\n\n\n

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