Monotone Subsystem Decomposition for Efficient Multi-Objective Robot Design

TL;DR

This paper introduces a novel monotone subsystem decomposition technique that efficiently computes Pareto fronts for large-scale multi-objective robot design problems, enabling faster and scalable optimization.

Contribution

It extends constraint programming with a new decomposition method to improve scalability and reusability in multi-objective robot component selection problems.

Findings

Scales better than linear programming for large catalogs

Solves multi-objective problems with 10^25 combinations in seconds

Enables rapid design of robot fleets with optimized schedules

Abstract

Automating design minimizes errors, accelerates the design process, and reduces cost. However, automating robot design is challenging due to recursive constraints, multiple design objectives, and cross-domain design complexity possibly spanning multiple abstraction layers. Here we look at the problem of component selection, a combinatorial optimization problem in which a designer, given a robot model, must select compatible components from an extensive catalog. The goal is to satisfy high-level task specifications while optimally balancing trade-offs between competing design objectives. In this paper, we extend our previous constraint programming approach to multi-objective design problems and propose the novel technique of monotone subsystem decomposition to efficiently compute a Pareto front of solutions for large-scale problems. We prove that subsystems can be optimized for their Pareto fronts and, under certain conditions, these results can be used to determine a globally optimal Pareto front. Furthermore, subsystems serve as an intuitive design abstraction and can be reused across various design problems. Using an example quadcopter design problem, we compare our method to a linear programming approach and demonstrate our method scales better for large catalogs, solving a multi-objective problem of 10^25 component combinations in seconds. We then expand the original problem and solve a task-oriented, multi-objective design problem to build a fleet of quadcopters to deliver packages. We compute a Pareto front of solutions in seconds where each solution contains an optimal component-level design and an optimal package delivery schedule for each quadcopter.