【摘要】 针对基于视觉测量的传统自标定方法存在可测量区域较小，仅能对空间机械臂局部工作空间内的位姿进行测量的问题，提出了一种基于多视觉靶标分布的自标定方法。通过分析手眼相机可测量区域大小与靶标数量及位置的关系，基于可观测性指数最大的目标优选多个视觉靶标的分布信息，实现在多个区域内对机械臂的末端位姿测量，最后对7自由度空间机械臂进行仿真实验。实验结果表明：单视觉靶标测量方案位置平均误差降低了70.68%,姿态平均误差降低了72.41%;多视觉靶标测量方案位置平均误差降低了88.66%,姿态平均误差降低了84.17%。相比于单视觉靶标测量方案，基于多视觉靶标的测量方案更能表征误差的影响能力，提高了标定结果的可靠性与全局最优性，验证了基于多视觉靶标分布的自标定方案的有效性。更多还原

【Abstract】 A self-calibration method based on multi-visual target distribution was proposed to address the problems of traditional self-calibration methods based on visual measurement, such as small measurable area and only measuring the pose within the local workspace of space robot. By analyzing the relationship between the size of the measurable area of the hand-eye camera and the number and position of targets, and selecting the distribution information of multi-visual targets based on the target with the highest observability index, the end pose of the robot can be measured in multiple areas. Finally, simulation experiments were conducted on a 7-degree-of-freedom space robot. The experimental results showed that the average position error and attitude error of the single vision target measurement scheme were reduced by 70.68% and 72.41%, respectively. The average position error and attitude error of the multi-vision target measurement scheme were reduced by 88.66% and 84.17%, respectively. Compared with the single visual target measurement scheme, the measurement scheme based on multi-visual targets could better characterize the influence of errors, improve the reliability and global optimality of calibration results. The effectiveness of the self-calibration scheme based on the distribution of multi-visual targets was verified.更多还原