Abstract
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, the existing research for 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, exhaustive search, genetic algorithms, and stochastic gradient descent and 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 success rate solving over 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.
Abstract (translated)
机器人自动化是提高制造过程效率和灵活性的关键技术。然而，在新的环境中部署机器人时，一个挑战在于找到机器人的最优基座姿态（base pose），这会影响其可达性和部署成本。不过，现有的关于自动优化机器人基座姿态的研究尚未进行过比较研究。我们通过使用贝叶斯优化、穷举搜索、遗传算法和随机梯度下降来优化工业机器人的基座姿态，并发现所有这些算法都能在合成环境和真实世界环境中减少各种任务的循环时间。随机梯度下降法表现出色，成功解决了超过90%的真实世界任务；而遗传算法则显示出最低的最终成本。所有的基准测试和实现方法都可以作为新方法进行比较的基础。
URL
https://arxiv.org/abs/2504.19577
