This paper presents a hybrid trajectory optimization method designed to generate collision-free, smooth trajectories for autonomous mobile robots. By combining sampling-based Model Predictive Path Integral (MPPI) control with gradient-based Interior-Point Differential Dynamic Programming (IPDDP), we leverage their respective strengths in exploration and smoothing. The proposed method, MPPI-IPDDP, involves three steps: First, MPPI control is used to generate a coarse trajectory. Second, a collision-free convex corridor is constructed. Third, IPDDP is applied to smooth the coarse trajectory, utilizing the collision-free corridor from the second step. To demonstrate the effectiveness of our approach, we apply the proposed algorithm to trajectory optimization for differential-drive wheeled mobile robots and point-mass quadrotors. In comparisons with other MPPI variants and continuous optimization-based solvers, our method shows superior performance in terms of computational robustness and trajectory smoothness. Code: https://github.com/i-ASL/mppi-ipddp Video: https://youtu.be/-oUAt5sd9Bk

翻译：本文提出了一种混合轨迹优化方法，旨在为自主移动机器人生成无碰撞且平滑的轨迹。通过将基于采样的模型预测路径积分控制与基于梯度的内点微分动态规划相结合，我们利用了二者分别在探索和平滑方面的优势。所提出的MPPI-IPDDP方法包含三个步骤：首先，使用MPPI控制生成一条粗略轨迹。其次，构建一个无碰撞的凸形走廊。最后，应用IPDDP对粗略轨迹进行平滑处理，该步骤利用了第二步得到的无碰撞走廊。为了证明我们方法的有效性，我们将所提算法应用于差速驱动轮式移动机器人和点质量四旋翼飞行器的轨迹优化。与其他MPPI变体以及基于连续优化的求解器相比，我们的方法在计算鲁棒性和轨迹平滑性方面均表现出更优的性能。代码：https://github.com/i-ASL/mppi-ipddp 视频：https://youtu.be/-oUAt5sd9Bk