Distributed assembly with online workload balancing and visual error detection and correction

We consider the assembly of a three-dimensional (3D) structure by a team of heterogeneous robots capable of online sensing and error correction during the assembly process. We build on our previous work and address the partitioning of the assembly task to maximize parallelization of the assembly process. Specifically, we consider 3D structures that can be assembled from a fixed collection of heterogeneous tiles that vary in shapes and sizes. Given a desired 3D structure, we first compute the partition of the assembly strategy into N-a subcomponents that can be executed in parallel by a team of N-a assembly robots. The assembly robots then perform online workload balancing during construction to minimize assembly time. To enable online error detection and correction during the assembly process, mobile robots equipped with visual depth sensors are tasked to scan, identify, and track the state of the structure. The result is a cooperative assembly framework where assembly robots can balance their individual workloads online by trading assembly components while scanning robots detect and reassign missing assembly components online. We present the integration of the planning, sensing, and control strategies employed in our framework and report on the experimental validation of the strategy using our multi-robot testbed.