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Andrew Wilhelm

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3 papers

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ICRA Conference 2025 Conference Paper

Monotone Subsystem Decomposition for Efficient Multi-Objective Robot Design

Andrew Wilhelm
Nils Napp

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.

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IROS Conference 2024 Conference Paper

Frozen Assets: Leveraging Ice, Water, and Phase Transitions in Robots

Aaron Wilhelm
Andrew Wilhelm
Lydia Isabela Calderon-Aceituno
Nils Napp
Kirstin Petersen
E. Farrell Helbling

Robots are especially useful in cold, remote, and inhospitable environments such as polar regions and extraterrestrial settings. Due to subfreezing temperatures and limited resources in these environments, robots made of ice are particularly advantageous. In this paper we demonstrate how the solid and liquid phases of water, and transitions between these phases, can be leveraged into common robot designs for modular robots, robot arms, rovers, and soft robots. We explore how robots can utilize structural elements made of ice and exploit the phase change between ice and water to augment their capabilities. Additionally, we do a scaling analysis of ice structural elements to provide insight on their performance at different length scales and ambient temperatures.

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IROS Conference 2023 Conference Paper

Constraint Programming for Component-Level Robot Design

Andrew Wilhelm
Nils Napp

Effective design automation for building robots would make development faster and easier while also less prone to design errors. However, complex multi-domain constraints make creating such tools difficult. One persistent challenge in achieving this goal of design automation is the fundamental problem of component selection, an optimization problem where, given a general robot model, components must be selected from a possibly large set of catalogs to minimize design objectives while meeting target specifications. Different approaches to this problem have used Monotone Co-Design Problems (MCDPs) or linear and quadratic programming, but these require judicious system approximations that affect the accuracy of the solution. We take an alternative approach formulating the component selection problem as a combinatorial optimization problem, which does not require any system approximations, and using constraint programming (CP) to solve this problem with a depth-first branch-and-bound algorithm. As the efficacy of CP critically depends upon the orderings of variables and their domain values, we present two heuristics specific to the problem of component selection that significantly improve solve time compared to traditional constraint satisfaction programming heuristics. We also add redundant constraints to the optimization problem to further improve run time by evaluating certain global constraints before all relevant variables are assigned. We demonstrate that our CP approach can find optimal solutions from over 20 trillion candidate solutions in only seconds, up to 48 times faster than an MCDP approach solving the same problem. Finally, for three different robot designs we build the corresponding robots to physically validate that the selected components meet the target design specifications.

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