Simulating Diverse Robot Motion Trajectories Using Simulink

Efficient and accurate trajectory generation is critical in robotic motion planning, particularly for high-degree-of-freedom manipulators. While various trajectory generation methods exist, a systematic comparative analysis within a unified simulation framework remains unexplored. This research bridges this gap by implementing and evaluating four trajectory generation methods—trapezoidal, cubic polynomial, quintic polynomial, and B-spline—on the Kinova Gen3 V12, a 7-DOF robotic manipulator, within MATLAB’s Simulink environment. The study introduces a high-fidelity simulation model incorporating dynamic constraints such as varying payloads (±5 kg) and velocity limits (±10°/s and ±20°/s) to analyze the robustness of each trajectory method. Performance is assessed using trajectory smoothness and computational efficiency. Results indicate that the B-spline method provides the smoothest motion with a minimal jerk, making it ideal for tasks requiring continuous movement. However, its higher computational cost may restrict its use in real-time applications. The trapezoidal trajectory, while computationally efficient, exhibits abrupt transitions, leading to increased mechanical stress on robotic joints. The quintic polynomial trajectory balances smoothness and efficiency, proving suitable for precision-oriented tasks. This research uniquely integrates trajectory modeling, simulation, and performance evaluation within Simulink, providing a comprehensive benchmarking framework for robotic trajectory planning. These findings offer actionable insights for robotics engineers and researchers, aiding in optimal trajectory selection for industrial automation, assistive robotics, and human-robot collaboration. Future work will explore real-time implementation and hybrid trajectory planning techniques to further enhance robotic motion planning strategies.