Robot Co-design: Beyond the Monotone Case
This addresses the need for inexpensive, task-specific robots in applications like scientific discovery and search-and-rescue, offering an incremental improvement over existing methods.
The paper tackles the computational robot co-design problem by proposing a binary optimization formulation that generalizes prior work and solves large problems in seconds, demonstrating it on autonomous drone racing and multi-robot systems.
Recent advances in 3D printing and manufacturing of miniaturized robotic hardware and computing are paving the way to build inexpensive and disposable robots. This will have a large impact on several applications including scientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g., operation in confined spaces), and entertainment (e.g., nano drones). The need for inexpensive and task-specific robots clashes with the current practice, where human experts are in charge of designing hardware and software aspects of the robotic platform. This makes the robot design process expensive and time-consuming, and ultimately unsuitable for small-volumes low-cost applications. This paper considers the computational robot co-design problem, which aims to create an automatic algorithm that selects the best robotic modules (sensing, actuation, computing) in order to maximize the performance on a task, while satisfying given specifications (e.g., maximum cost of the resulting design). We propose a binary optimization formulation of the co-design problem and show that such formulation generalizes previous work based on strong modeling assumptions. We show that the proposed formulation can solve relatively large co-design problems in seconds and with minimal human intervention. We demonstrate the proposed approach in two applications: the co-design of an autonomous drone racing platform and the co-design of a multi-robot system.