Experimental validation of jerk-minimized trajectory planning in a 4-DOF industrial robotic arm using inverse kinematics with root multiplicity

Purpose In high-precision industrial robotics, sudden changes in acceleration (jerk) often induce vibrations, increase mechanical wear and compromise accuracy. To address these issues, this study aims to introduce a jerk-minimized trajectory planning framework for a 4-degree-of-freedom (DOF) robotic arm, integrating root multiplicity directly into the inverse kinematics formulation. Design/methodology/approach A hybrid simulation–experimental framework was developed in this study. MATLAB was used to compute joint parameters using segmented polynomial profiles, where root multiplicity was enforced to ensure zero-acceleration boundary conditions and suppress jerk. The optimized joint trajectories were executed on an Arduino-controlled 4-DOF robotic arm to perform a three-dimensional pick-and-place task under low-load execution conditions, with experimental validation presented as a controlled proof-of-concept demonstration. Comparative evaluations were conducted against conventional trapezoidal point-to-point (PTP) and quintic polynomial trajectory planning methods. Findings The proposed method achieved a reduction of up to 87% in the peak jerk and a 64% improvement in the end-effector path accuracy compared with conventional trapezoidal PTP trajectories, with additional enhancements observed over quintic polynomial methods. Originality/value Unlike conventional spline- or quintic-based approaches, the proposed framework leverages root multiplicity for a computationally efficient and analytically tractable solution that directly integrates with inverse kinematics. Its successful implementation on low-cost hardware demonstrates strong potential for scalable, real-time deployment in Industry 5.0 applications such as assembly, inspection, and human–robot collaboration.