  This article presents a multi-robot trajectory planning method which not only
guarantees optimization feasibility and but also resolves deadlocks in
obstacle-dense environments. The method is proposed via formulating a recursive
optimization problem, where a novel safe corridor is generated online to ensure
obstacle avoidance in trajectory planning. A dynamic-priority mechanism is
combined with the right-hand rule to handle potential deadlocks that are much
harder to resolve due to static obstacles. Comparisons with other
state-of-the-art results are conducted to validate the improved safety and
success rate. Additional hardware experiments are carried out with up to eight
nano-quadrotors in various cluttered scenarios.
