论文标题
MPPI-IPDDP：无碰撞平滑轨迹生成的混合方法
MPPI-IPDDP: Hybrid Method of Collision-Free Smooth Trajectory Generation for Autonomous Robots
论文作者
论文摘要
本文提出了一种混合轨迹优化方法，旨在为自动移动机器人生成无碰撞，平滑的轨迹。通过将基于采样的模型预测路径积分（MPPI）控制与基于梯度的内点差分动态编程（IPDDP）相结合，我们利用它们在探索和平滑方面的优势。提出的方法MPPI-IPDDP涉及三个步骤：首先，MPPI控制用于生成粗轨迹。其次，构建了无碰撞凸走道。第三，使用IPDDP来平滑粗轨迹，利用第二步的无冲突走廊。为了证明我们的方法的有效性，我们将提出的算法应用于差速器驱动的车轮移动机器人和点质量四四个四方的轨迹优化。与其他MPPI变体和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度方面表现出卓越的性能。 代码：https：//github.com/i-asl/mppi-ipddp视频：https：//youtu.be/-ouat5sd9bk
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
