Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production Lines

Abstract

In automotive production cells, complex processes involving multiple robots must be optimized for cycle time. We investigated using symbolic GR(1) controller synthesis for automating multi-robot task planning. Given a specification of the order of tasks and states to avoid, often multiple valid strategies can be computed; in many states there are multiple choices to satisfy the specification, such as choosing different robots to perform a certain task. To determine the best choices under the consideration of movement times and probabilities that robots may be interrupted for repairs or corrections, we combine the execution of the synthesized controller with Monte Carlo Tree Search (MCTS), a heuristic AI-planning technique. The result is a model-at-run-time approach that we present by the example of a multi-robot spot welding cell. We report on experiments showing that the approach (1) can reduce cycle times by choosing time-efficient movement sequences and (2) can choose executions that react efficiently to interruptions by choosing to delay tasks that, if an interruption of one robot should occur later, can be reallocated to another robot. Most interestingly, we found, however, that (3) in some cases there is a conflict between time-efficient movement sequences and ones that may react efficiently to probable future interruptions—and when interruption probabilities are low, increasing the time allocated for MCTS, i.e., increasing the number of sample simulations made by MCTS, does not improve cycle time.