Biased-MPPI：通过融合辅助控制器为基于采样的模型预测控制提供信息
Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers
AI总结：
本文提出Biased-MPPI方法，通过允许任意采样分布并融合经典与学习型辅助控制器，解决传统MPPI易陷入局部极小值的问题，提升采样效率、鲁棒性与收敛性。
摘要：
动态环境中自主机器人的运动规划由于机器人动力学及其与其他智能体交互的不确定性而面临诸多挑战。基于采样的MPC方法，例如模型预测路径积分（MPPI）控制，在解决这些复杂运动规划问题方面已显示出潜力。然而，MPPI的性能在很大程度上依赖于采样分布的选择。现有文献通常使用先前计算的输入序列作为高斯分布的均值进行采样，这可能导致失败和局部极小值。在本文中，我们提出了一种新的MPPI推导方法，允许任意采样分布以提高效率、鲁棒性和收敛性，同时缓解局部极小值问题。我们提出了一种高效的重要性采样方案，该方案同时结合经典的和基于学习的辅助控制器，从而实现更具信息量的采样和控制融合。多个仿真和真实世界实验证明了我们方法的有效性。
英文摘要：
Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex motion planning problems. However, the performance of MPPI relies heavily on the choice of sampling distribution. Existing literature often uses the previously computed input sequence as the mean of a Gaussian distribution for sampling, leading to potential failures and local minima. In this paper, we propose a novel derivation of MPPI that allows for arbitrary sampling distributions to enhance efficiency, robustness, and convergence while alleviating the problem of local minima. We present an efficient importance sampling scheme that combines classical and learning-based ancillary controllers simultaneously, resulting in more informative sampling and control fusion. Several simulated and real-world demonstrate the validity of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
