🤖 AI Summary
This work addresses the limitations of classical trajectory planning methods, which prioritize kinematic smoothness while neglecting dynamics and actuator control effort, often resulting in large tracking errors and high energy consumption. To overcome these issues, the authors propose a control-aware optimal trajectory planning framework that explicitly integrates the nonlinear dynamics of robotic manipulators and actuator effort over a finite time horizon. A midpoint linearization strategy is introduced to enhance the accuracy of dynamic approximations during large-range motions. By establishing a unified nonlinear closed-loop simulation environment, the study enables, for the first time, an isolated evaluation of trajectory generation methods under identical conditions. Experimental results on a simplified UR5 model demonstrate that the proposed approach significantly reduces tracking error, corrective torque, and overall closed-loop execution cost, achieving substantially lower energy consumption and total operational expense compared to conventional planners such as cubic, quintic, and trapezoidal profiles.
📝 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.