Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators
This paper presents a control-aware optimal trajectory planning framework that incorporates manipulator dynamics and actuator effort. A midpoint linearization strategy improves accuracy for large motions. Simulations on a UR5 robot show consistent reductions in tracking error, corrective torque, and execution cost compared to classical kinematic planners, demonstrating that kinematic smoothness does not guarantee dynamic efficiency.
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[Submitted on 6 Jul 2026]
Title:Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators
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Abstract: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. As a 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.
Comments: Accepted at MERCon 2026. To be presented at MERCon 2026. 7 pages, 7 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.05544 [cs.RO]
(or arXiv:2607.05544v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.05544
arXiv-issued DOI via DataCite
Submission history
From: Bhanuka Dayawansa [view email] [v1] Mon, 6 Jul 2026 18:26:48 UTC (2,397 KB)
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