A Collision Avoidance Planning Algorithm in Cluttered Environments with Moving Obstacles

This paper investigates the collision avoidance planning problem for Mecanum-wheeled robot in cluttered environments with moving obstacles. The challenge lies in that the motions of obstacles are not available and hard to be predicted. First, a IMM-based algorithm is developed to obtain motion model and states of moving obstacles. Then, an optimization-based planning algorithm is proposed to achieve collision avoidance in which the planning problem is transformed into an unconstrained optimization problem, which can be solved by g2o-framework. The simulation experiments are performed to show the effectiveness of the proposed planning algorithm.