Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance - Xu, Guangtong; Long, Teng; Wang, Zhu; Sun, Jingliang (ISA transactions, 2022)
This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP. •The trust-region filter is developed for SCP to construct low-complexity subproblems, which can enhance the computational efficiency.•The adaptive trust-region updating mechanism is designed to avoid infeasible iteration of TRF-SCP.•The effectiveness of TRF-SCP is verified on contrastive simulations and flight experiments.