Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system’s flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to generate a budget-minimizing layout by selectively placing stationary robots on a floor grid to sort packages from given input and output locations. Finding a good layout that minimizes the hardware budget while ensuring motion feasibility is a challenging combinatorial problem with nonconvex motion constraints. We propose a new optimization-based approach that models layout planning as a subgraph optimization problem subject to network flow constraints. Our core insight is to abstract away motion constraints from the layout optimization by precomputing a kinematic reachability graph and then extract the optimal layout on this ground graph. We validate the motion feasibility of our approach by proposing a simple task assignment and motion planning technique. We benchmark our algorithm on problems with various grid resolutions and number of outputs and show improvements in memory efficiency over a heuristic search algorithm. In addition, we demonstrate that our algorithm can be extended to handle various types of robot manipulators and conveyor belts, box payload constraints, and cost assignments.

Temporary bar structures made of reusable standardized components are widely used in construction, events, and exhibitions. They are economical, easy to assemble, and can be disassembled and reused in various structural arrangements for various purposes. However, existing reusable temporary structures are either limited to modular yet repetitive designs or require bespoke components, which restricts their reuse potential. Instead of designing bespoke kit of parts for limited reuse, this paper investigates how to design and build diverse freeform structures from one homogeneous kit of parts. We propose a computational framework to generate multi-tangent bar structures, a widely used jointing system, which allows bars to be joined at any point along their length with standard connectors. We present a mathematical formulation and a numerical scheme to optimize the bar spatial positions and contact assignment simultaneously, while ensuring that the constraints of tangency, collision, joint connectivity, and bar length are satisfied. Together with simulated case studies, we present two physical prototypes that reuse the same kit of parts using an augmented reality-guided assembly workflow. • Computational design of diverse freeform structures from uniform kit of parts. • Modeling of tangency, collision, connectivity, and bar-length constraints. • Simultaneous optimization of geometry and connectors for uniform bars and couplers. • Augmented reality-assisted rapid prefabrication and on-site assembly process. • Two pavilion-scale prototypes demonstrate reusability of one parts kit.

We present a computational framework for planning the assembly sequence of bespoke frame structures. Frame structures are one of the most commonly used structural systems in modern architecture, providing resistance to gravitational and external loads. Building frame structures requires traversing through several partially built states. If the assembly sequence is planned poorly, these partial assemblies can exhibit substantial deformation due to self-weight, slowing down or jeopardizing the assembly process. Finding a good assembly sequence that minimizes intermediate deformations is an interesting yet challenging combinatorial problem that is usually solved by heuristic search algorithms. In this paper, we propose a new optimization-based approach that models sequence planning using a series of topology optimization problems. Our key insight is that enforcing temporal coherent constraints in the topology optimization can lead to sub-structures with small deformations while staying consistent with each other to form an assembly sequence. We benchmark our algorithm on a large data set and show improvements in both performance and computational time over greedy search algorithms. In addition, we demonstrate that our algorithm can be extended to handle assembly with static or dynamic supports. We further validate our approach by generating a series of results in multiple scales, including a real-world prototype with a mixed reality assistant using our computed sequence and a simulated example demonstrating a multi-robot assembly application.

Frame shapes, which are made of struts, have been widely used in many fields, such as art, sculpture, architecture, and geometric modeling, etc. An interest in robotic fabrication of frame shapes via spatial thermoplastic extrusion has been increasingly growing in recent years. In this paper, we present a novel algorithm to generate a feasible fabrication sequence for general frame shapes. To solve this non-trivial combinatorial problem, we develop a divide-and-conquer strategy that first decomposes the input frame shape into stable layers via a constrained sparse optimization model. Then we search a feasible sequence for each layer via a local optimization method together with a backtracking strategy. The generated sequence guarantees that the already-printed part is in a stable equilibrium state at all stages of fabrication, and that the 3D printing extrusion head does not collide with the printed part during the fabrication. Our algorithm has been validated by a built prototype robotic fabrication system made by a 6-axis KUKA robotic arm with a customized extrusion head. Experimental results demonstrate the feasibility and applicability of our algorithm.