论文标题
基于多样性的大型机器人的设计协助
Diversity-based Design Assist for Large Legged Robots
论文作者
论文摘要
我们将地图精英和高度可行的模拟结合在一起，以探索一类大腿机器人的设计空间，该机器人高约2m，其设计和构造尚未得到充分研究。修改模拟以说明电动机扭矩和重量等因素，并提供合理的保真度搜索空间。一种新型的机器人编码允许生物启发的特征，例如沿着身体长度缩放的腿。在人体脑的共同进化中评估了三种可能的控制生成方案的影响，表明即使受到促进启动机制的限制性问题也受到强大的好处。实施的两个阶段过程。在第一阶段，生成了可能的机器人库，将用户要求视为约束。在第二阶段，分析了最有前途的机器人壁ni，并与其特征变量值相关的人为理解的设计规则套件。然后，这些规则与图书馆一起准备被（人）机器人设计师用作设计辅助工具。
We combine MAP-Elites and highly parallelisable simulation to explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not well-studied. The simulation is modified to account for factors such as motor torque and weight, and presents a reasonable fidelity search space. A novel robot encoding allows for bio-inspired features such as legs scaling along the length of the body. The impact of three possible control generation schemes are assessed in the context of body-brain co-evolution, showing that even constrained problems benefit strongly from coupling-promoting mechanisms. A two stage process in implemented. In the first stage, a library of possible robots is generated, treating user requirements as constraints. In the second stage, the most promising robot niches are analysed and a suite of human-understandable design rules generated related to the values of their feature variables. These rules, together with the library, are then ready to be used by a (human) robot designer as a Design Assist tool.
