比较四种算法优化机器人基座位姿，提升部署效率与成功率。
Smart placement, faster robots-a comparison of algorithms for robot base-pose optimization
- 用贝叶斯优化、遗传算法等四类方法自动寻找最优基座位置。
- 随机梯度下降在真实场景中任务成功率超90%，表现最佳。
- 适合想快速验证新算法的机器人部署研究者参考。
机器人自动化是提升制造过程效率与灵活性的关键技术。然而，在新环境中部署机器人时，确定机器人的最优基座位姿仍是一大挑战，因其直接影响可达性与部署成本。现有研究尚未对自动优化基座位姿的方法进行系统比较。本文采用贝叶斯优化（BO）、穷举搜索（ES）、遗传算法（GAs）和随机梯度下降（SGD）优化工业机器人基座位姿，并在合成与真实场景中验证了各类算法均能有效缩短任务周期时间。其中，随机梯度下降在真实任务中成功率超过90%，表现最优；而遗传算法最终成本最低。所有基准测试与实现方法均已开源，可作为新方法的对比基线。
原文摘要 · Abstract (English)
Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the challenges in deploying robots in novel environments is finding the optimal base pose for the robot, which affects its reachability and deployment cost. Yet, existing research on automatically optimizing the base pose of robots has not been compared. We address this problem by optimizing the base pose of industrial robots with Bayesian optimization (BO), exhaustive search (ES), genetic algorithms (GAs), and stochastic gradient descent (SGD), and we find that all algorithms can reduce the cycle time for various evaluated tasks in synthetic and real-world environments. Stochastic gradient descent shows superior performance with regard to the success rate, solving more than 90% of our real-world tasks, while genetic algorithms show the lowest final costs. All benchmarks and implemented methods are available as baselines against which novel approaches can be compared.
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