MultiRobot Assembly Sequencing via Discrete Optimization Multirobot assembly has the potential to greatly reduce the cost and risk associated with the fabrication of large structures. Using teams of robots to perform assembly offers numerous advantages such as parallelism, robustness to singleagent failures, and flexibility in scheduling and task assignment. However, while previous work on multirobot assembly focuses on generating feasible assembly plans and decentralized control strategies, we instead study the problem of planning optimal assembly sequences.To this end, we pose the problem of multirobot assembly as a discrete optimization, specifically an integer linear program (ILP) or quadratic program (IQP), which aims to minimize the time to complete the assembly, or to minimize the distance traveled. We develop a model of multirobot assembly that captures both geometric constraints and actuation constraints inherent to the problem. While the ILP and IQP can be solved exactly using commercial optimization software in a substantial amount of time, we also propose heuristic strategies which can be quickly computed, and can scale to structures of reasonable size. We also verify our methods empirically by comparing their performance on a variety of test structures.

Integer Programming as a General Solution Methodology for PathBased Optimization in Robotics: Principles, Best Practices, and Applications Integer programming (IP) has proven to be highly effective in solving many pathbased optimization problems in robotics. However, the applications of IP are generally done in an adhoc, problemspecific manner. In this work, after examined a wide range of pathbased optimization problems, we describe an IP solution methodology for these problems that is both easy to apply (in two simple steps) and highperformance in terms of the computation time and the achieved optimality. We demonstrate the generality of our approach through the application to three challenging pathbased optimization problems: multirobot path planning(MPP), minimum constraint removal(MCR), and reward collection problems(RCPs). Associated experiments show that the approach can efficiently produce (near)optimal solutions for problems with large state spaces, complex constraints, and complicated objective functions. In conjunction with the proposition of the IP methodology, we introduce two new and practical robotics problems: multirobot minimum constraint removal(MMCR) and multirobot path planning(MPP) with partial solutions, which can be quickly and effectively solved using our proposed IP solution pipeline.