Time optimal trajectory planning of robotic arm based on improved hippo optimization algorithm

Abstract Trajectory planning is essential for ensuring the efficient and precise operation of actuators by optimizing motion paths, which is particularly crucial in fields such as robotics, machine tools, and automated production lines. This optimization enhances system efficiency, reduces energy consumption, and maintains product quality consistency. Consequently, the trajectory planning problem warrants thorough investigation. To enable a manipulator to complete designated tasks in the shortest possible time while adhering to kinematic constraints, we propose a time-optimal 3-5-3 piecewise polynomial interpolation trajectory planning algorithm based on an enhanced Hippo algorithm. This study focuses on a six-axis industrial manipulator as the research subject; it constructs a mathematical model that optimizes upon the standard Hippo algorithm by incorporating chaotic mapping and dynamic inertia weight enhancements to achieve improved performance in time-optimal trajectory planning. Finally, simulations are conducted using MATLAB software. The results demonstrate that the improved Hippo algorithm outperforms its traditional counterpart regarding convergence and optimization efficiency, with an optimized motion duration of approximately 3.6 seconds—indicating that this enhancement is effective, reliable, and practical within relevant domains.