Multi-robot planner improves flexible assembly without fixtures
WRAP: Fixtureless Wrench-aware Multi-Robot Assembly Planning
Robotics
Summary
Robotic assembly often needs special holding tools to keep parts steady, or it only works by building from the top down. The authors created Wrap, a way for several robots to work together building things without extra supports, by carefully planning who does what and when. Their method checks the forces on the parts during assembly to decide if robots need to hold pieces or if surfaces like tables can help. Wrap plans the assembly step-by-step, figuring out feasible robot actions quickly, and has been tested both in computer simulations and with real robots.
What this means in practice
- •For robotics engineers: Coordinate multiple robots to assemble complex parts without the need for custom fixtures by planning both the sequence and force support.
- •For factory automation teams: Deploy flexible robotic cell setups that handle diverse assembly tasks without redesigning fixtures for each new product.
Authors
Valentin N. Hartmann, Huang Su, Yijiang Huang, Stelian Coros
Abstract
Assembly using robots often requires specially designed fixtures, or relies on top-down only assembly strategies. Using multiple robots, we can avoid using fixtures and make robotic assembly more flexible. Planning assembly sequences for multiple robots is challenging due to the high number of possible task assignments and orders. In addition, we need to reason over forces that occur during the assembly process, e.g., to decide if multiple robots are required for support, or if external support such as a table should be used. We present Wrap, a multi-robot assembly planner for multi-part assemblies, given the inter-part ordering-dependencies, the part meshes, and their initial state. We formulate a linear program to reason about valid grasps for supporting the forces that occur during assembly. The search leverages the assembly sequence, and greedily finds a feasible solution per assembly step by computing a heuristic via a cheap backwards search, and using the heuristic in the more expensive forward search. We then solve the multi-robot, multi-goal motion planning problem, and for execution, we split the plan into contact-rich assembly skills, and free space motion. We benchmark the planner on a variety of multi-part assemblies, and apply the planner to groups of robots differing in size and kinematics. We validate the work both in a physics simulation, and in real. Videos and code are available at https://www.vhartmann.com/wrap.