让机器人在人群中安全行走，能实时计算碰撞风险并避开高危路径。
Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
- 用蒙特卡洛方法高效估算多障碍物的联合碰撞概率
- 在几百条轨迹中实时筛选或加权低风险路径，避免机器人僵住
- 适合需在人群里灵活避障的移动机器人，如配送或导览机器人
在人类密集环境中部署移动机器人，要求运动规划器能够考虑其他智能体轨迹的不确定性。传统方法在此类问题上仍面临挑战，尤其在处理任意形状预测和实时性约束时。为此，我们提出动态风险感知模型预测路径积分控制（DRA-MPPI），一种将非高斯随机预测纳入考量的运动规划方法。利用MPPI无需梯度的特性，我们通过蒙特卡洛方法高效近似数百条采样轨迹的联合碰撞概率（CP），实现在实时条件下拒绝超过预设阈值的样本，或将其作为导航代价函数中的加权项。该方法有效缓解了机器人冻结问题，提升了安全性。真实场景与模拟实验表明，相比最先进的方法（包括情景式模型预测控制、弗雷内规划器和标准MPPI），DRA-MPPI表现出更优性能。
原文摘要 · Abstract (English)
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic predictions. By leveraging MPPI's gradient-free nature, we propose a method that efficiently approximates the joint Collision Probability (CP) among multiple dynamic obstacles for several hundred sampled trajectories in real-time via a Monte Carlo (MC) approach. This enables the rejection of samples exceeding a predefined CP threshold or the integration of CP as a weighted objective within the navigation cost function. Consequently, DRA-MPPI mitigates the freezing robot problem while enhancing safety. Real-world and simulated experiments with multiple dynamic obstacles demonstrate DRA-MPPI's superior performance compared to state-of-the-art approaches, including Scenario-based Model Predictive Control (S-MPC), Frenet planner, and vanilla MPPI.
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