Comprehensive shape optimization based on reliability for a robot arm

This study presents a multi-objective trajectory planning optimization of an industrial robotic arm. Integrating stress analysis with trajectory planning and power consumption to enhance the structural integrity and energy efficiency. Joint trajectories are modeled using sixth-degree polynomial functions to ensure smooth, continuous rest-to-rest motion. A genetic algorithm is employed to minimize energy consumption while evaluating the mechanical performance of various link cross-sectional geometries (circular, square, rectangular, and elliptical), each considered in both hollow and solid forms. Stress analyses were conducted under both static and dynamic (fatigue-based) conditions, incorporating realistic factors such as link self-weight, actuators weight, maximum payload, and motor torque to accurately predict stress distributions and fatigue life. Hollow rectangular cross-sections achieved the highest performance, reducing weight and energy consumption by up to 30% and 27%, respectively, compared to circular ones. A lower hollow-rectangular aspect ratio e = 0.15 further reduced energy use by 34% relative to e = 0.5. Accelerated trajectories (0.5 s vs. 5 s) led to 18% mass increase, highlighting a trade-off between speed and structural efficiency. This integrated approach identifies design configurations that achieve an optimal balance between structural durability, mass efficiency, and torque performance while significantly reducing energy consumption.