论文标题
风险感知模型的预测路径积分控制使用条件价值风险
Risk-Aware Model Predictive Path Integral Control Using Conditional Value-at-Risk
论文作者
论文摘要
在本文中，我们为自主机器人提供了一种新型的模型预测控制方法，但受任意形式的不确定性形式。拟议的风险感知模型预测路径积分（RA-MPPI）控制利用条件价值（CVAR）量度来为安全至关重要的机器人应用生成最佳控制动作。与大多数现有的随机MPC和CVAR优化方法不同，这些方法将原始动力学线性化并将控制任务制定为凸面程序，而拟议的方法直接使用原始动力学，而无需限制成本函数或噪声的形式。我们将新颖的RA-MPPI控制器应用于自动驾驶汽车，以在混乱的环境中进行侵略性的驾驶操作。我们的模拟和实验表明，与基线MPPI控制器相比，提出的RA-MPPI控制器可以达到左右的时间大约相同的圈时间。所提出的控制器以高达80Hz的更新频率执行在线计算，利用现代图形处理单元（GPU）来进行多线程轨迹以及CVAR值的生成。
In this paper, we present a novel Model Predictive Control method for autonomous robots subject to arbitrary forms of uncertainty. The proposed Risk-Aware Model Predictive Path Integral (RA-MPPI) control utilizes the Conditional Value-at-Risk (CVaR) measure to generate optimal control actions for safety-critical robotic applications. Different from most existing Stochastic MPCs and CVaR optimization methods that linearize the original dynamics and formulate control tasks as convex programs, the proposed method directly uses the original dynamics without restricting the form of the cost functions or the noise. We apply the novel RA-MPPI controller to an autonomous vehicle to perform aggressive driving maneuvers in cluttered environments. Our simulations and experiments show that the proposed RA-MPPI controller can achieve about the same lap time with significantly fewer collisions compared to the baseline MPPI controller. The proposed controller performs on-line computation at an update frequency of up to 80Hz, utilizing modern Graphics Processing Units (GPUs) to multi-thread the generation of trajectories as well as the CVaR values.
