Velocity Profile Generation for Industrial Robots Considering Natural Frequency Variations
Shingo Tajima et al.
What the paper says
Industrial robots are widely used to compensate for labor shortages and increase productivity. However, the natural frequency of a robot varies with its posture, making vibration suppression difficult. Residual vibration during operation increases the cycle time and reduces workpiece accuracy. Therefore, a velocity profile generation method that accounts for posture-dependent frequency variation is required to achieve high-precision robot machining. This study proposes a novel velocity profile generation method to suppress vibration in industrial robots with posture-dependent natural frequency variations. First, finite impulse response filtering and the jerk limited acceleration profile were applied to generate velocity profiles that remove different frequency components during the acceleration and deceleration phases. Next, two methods were developed for determining the suppressed frequencies: (1) filtering the natural frequencies at the start and end of the motion and (2) eliminating the frequency that minimizes the amplitude integral of the acceleration. The simulation results confirmed that the proposed trajectory generation method can reduce vibration, compared with the conventional filtering method, for both suppressed frequency-determination methods. The optimal velocity profile was achieved by filtering the frequency that minimizes the amplitude integral of the acceleration during the acceleration phase and the natural frequency at the end of the motion during the deceleration phase. The proposed method provides a practical solution for improving the dynamic performance of industrial robots by effectively suppressing vibration while maintaining motion efficiency.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.