Large-Scale ADMM-based Co-Design of Legged Robots

摘要

This paper considers the problem of designing legged robots for traversing uneven terrain, wherein terrain characteristics represent uncertainty for the design process. When this process encompasses a wider variety of terrains, the likelihood of the designed robot falling in the real world should decrease. However, computational scalability limits the number of terrains that can be taken into account during design. The proposed framework uses the Alternating Direction Method of Multipliers (ADMM) to solve large-scale concurrent design (co-design) problems. The ADMM coordinates the solution of small-size sub-problems and enforces constraints to reach a consensus on the best design. The framework uses stochastic programming (SP) to account for terrain uncertainty and trajectory optimization (TO) to co-optimize a nominal trajectory alongside hardware parameters and a feedback controller. Case studies demonstrate application for a monopod and a quadruped. For the monopod, ADMM facilitated an increase in the number of terrains considered within co-design by 400% compared to SP alone, which contributed to robustifying the design and decreasing its failure probability to under 1% in an anticipated operating space. A multi-scenario co-design implementation for the quadruped had previously been intractable due to scalability limitations. The ADMM framework, by contrast, shows tractability running with 30 terrain types, opening the horizon for designing more complex systems.