  We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time
industrial automation scenarios. Fast-dRRT* builds upon the discrete
rapidly-exploring random tree (dRRT*) planner, and extends dRRT* by using
pre-computed swept volumes for efficient collision detection, deadlock
avoidance for partial multi-robot problems, and a simplified rewiring strategy.
We evaluate Fast-dRRT* on five challenging multi-robot scenarios using two to
four industrial robot arms from various manufacturers. The scenarios comprise
situations involving deadlocks, narrow passages, and close proximity tasks. The
results are compared against dRRT*, and show Fast-dRRT* to outperform dRRT* by
up to 94% in terms of finding solutions within given time limits, while only
sacrificing up to 35% on initial solution cost. Furthermore, Fast-dRRT*
demonstrates resilience against noise in target configurations, and is able to
solve challenging welding, and pick and place tasks with reduced computational
time. This makes Fast-dRRT* a promising option for real-time motion planning in
industrial automation.
