Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Method Using Control Barrier Functions
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Method Using Control Barrier Functions
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DOI：
10.1109/lra.2023.3315211
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发表时间：
2023-02
影响因子：
5.2
通讯作者：
Ji Yin;Charles Dawson;Chuchu Fan;P. Tsiotras
中科院分区：
文献类型：
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作者：
Ji Yin;Charles Dawson;Chuchu Fan;P. Tsiotras
模型预测路径积分(MPPI)控制是一种基于采样的模型预测控制，它模拟数以千计的轨迹，并使用这些轨迹来动态合成最优控制。然而，在实践中，MPPI遇到了限制其应用的问题。例如，已经观察到，如果存在未建模的动态或环境干扰，MPPI往往会做出糟糕的决定，从而阻止其在安全关键应用中的使用。此外，MPPI使用的多线程模拟需要大量的板载计算资源，使得没有现代GPU的机器人无法访问该算法。为了缓解这些问题，我们提出了一种新的(Shield-MPPI)算法，该算法对不可预测的干扰具有鲁棒性，并在常规CPU上使用数量少得多的并行模拟来实现实时规划。新的Shield-MPPI算法在攻击型自主赛车平台上进行了仿真和硬件测试。结果表明，与最新的鲁棒MPPI变种和随机模型预测控制方法相比，所提出的控制器大大减少了约束违反次数。
Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesize optimal controls on-the-fly. In practice, however, MPPI encounters problems limiting its application. For instance, it has been observed that MPPI tends to make poor decisions if unmodeled dynamics or environmental disturbances exist, preventing its use in safety-critical applications. Moreover, the multi-threaded simulations used by MPPI require significant onboard computational resources, making the algorithm inaccessible to robots without modern GPUs. To alleviate these issues, we propose a novel (Shield-MPPI) algorithm that provides robustness against unpredicted disturbances and achieves real-time planning using a much smaller number of parallel simulations on regular CPUs. The novel Shield-MPPI algorithm is tested on an aggressive autonomous racing platform both in simulation and in hardware. The results show that the proposed controller greatly reduces the number of constraint violations compared to state-of-the-art robust MPPI variants and stochastic Model Predictive Control methods.