优化协作机器人安装位置，提升任务执行鲁棒性。
Optimizing Robot Positioning Against Placement Inaccuracies: A Study on the Fanuc CRX10iA/L
- 用粒子群算法搜索最优安装位置，结合逆运动学评估可行性。
- 通过Voronoi图计算最大内切圆，确定安全作业区域范围。
- 适合需高精度部署的工业场景，尤其移动基座安装时使用。
本研究提出一种针对Fanuc CRX10iA/L协作机器人在工业任务中轨迹执行的最优基座定位方法。采用粒子群优化算法探索可行位置空间，利用α-形状算法界定可行性区域边界，并基于Voronoi图计算最大内切圆以评估空间鲁棒性。该方法基于逆运动学模型评估初始构型，通过雅可比矩阵驱动末端执行器沿参考轨迹运动并评分。对于该机器人，逆运动学最多存在16个解，其求解复杂，现有规划工具如MoveIt难以全面覆盖。优化过程还考虑了关节限位、奇异点及工作空间约束，确保轨迹执行的可行性和高效性。
原文摘要 · Abstract (English)
This study presents a methodology for determining the optimal base placement of a Fanuc CRX10iA/L collaborative robot for a desired trajectory corresponding to an industrial task. The proposed method uses a particle swarm optimization algorithm that explores the search space to find positions for performing the trajectory. An $α$-shape algorithm is then used to draw the borders of the feasibility areas, and the largest circle inscribed is calculated from the Voronoi diagrams. The aim of this approach is to provide a robustness criterion in the context of robot placement inaccuracies that may be encountered, for example, if the robot is placed on a mobile base when the system is deployed by an operator. The approach developed uses an inverse kinematics model to evaluate all initial configurations, then moves the robot end-effector along the reference trajectory using the Jacobian matrix and assigns a score to the attempt. For the Fanuc CRX10iA/L robot, there can be up to 16 solutions to the inverse kinematics model. The calculation of these solutions is not trivial and requires a specific study that planning tools such as MoveIt cannot fully take into account. Additionally, the optimization process must consider constraints such as joint limits, singularities, and workspace limitations to ensure feasible and efficient trajectory execution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。